Bibliography for Process Mining and Phenotypes

Bibliography

This research bibliography pertains to the development of a reporting method for a managed care EMR.  The assumptions made for this research include the idea that EMR reporting processes may be made far more progressive by the development of a standardized scoring and evaluation system for reviewing nearly all of the most basic data and processes engaged in through healthcare.

Whereas disease diagnoses are quite different between patients, the processes of healthcare are assumed to be pretty much the same in spite of different diseases, managed care processes or even systems, and patient populations.  This allows comparisons to be made between the different parts of a managed care system.   The “standards” for specific care processes may then be defined, and compared between groups, populations, agencies, regions, etc.

This bibliography includes sections on:

  • Quality of Service/Quality of Care
  • Process Mining
  • Visual Analytics
  • Genomics-Phenomics and Quality
    • Genomics
    • Phenotypes
  • Applications
    • Translational/Transformational Medicine
    • Big Data and Cost, Machine Learning
    • Behavioral Health and Staging Care
    • Data Quality and Chronic Disease Management
    • Quality of Care Model
    • Personalized Medicine/Healthcare
    • Precision Medicine and Phenotyping
    • Health Care Processes
    • Diagnostic Code Methods
  • Modelling
  • Populations
    • Population Macrocosm-Protist Microcosm
    • Frailty (Aging Population)
    • Ageing
  • Pathology: Disease and Disorder Applications
    • Asthma/COPD
    • Atrial Fibrillation
    • Autism/Social Visual Engagement
    • Behavioral/Mental Health
    • Cancer
      • Breast
      • Colorectal
      • General
      • Leukemia
      • Lung
      • Melanoma
    • Cardiology
    • Chronic Disease
    • Chronic Pain
    • Clostridium difficile
    • Cystic Fibrosis
    • Crohn’s Disease
    • Diabetes
    • Hematological
    • Hypercholesterolemia
    • Infectious Diseases (incl. Spatial approach)
    • Kidney
    • Metabolic Phenotypes
    • Osteopathy
    • Parkinsonism
    • Pharmacogenomics
    • Pneumonia (Aspiration)
    • PTSD
    • Rheumatology
    • Schizophrenia
    • Sickle Cell
    • Sleep Apnea

 

BIBLIOGRAPHY:

Searches:

https://scholar.google.com/scholar?start=80&q=healthcare+patients+visits+procedures+&hl=en&as_sdt=1,33&as_ylo=2013

https://scholar.google.com/scholar?start=120&q=healthcare+modelling+patient+visits&hl=en&as_sdt=1,33&as_ylo=2013

Primary Subsearch for Process Mining techniques- https://scholar.google.com/scholar?start=20&q=related:24eJdM3sbfUJ:scholar.google.com/&hl=en&as_sdt=1,33&as_ylo=2013

 

References

Quality of Service/Quality of Care

Andersen RM, Davidson PL, Baumeister SE. Improving access to care. Changing the US health care system: Key issues in health services policy and management. 2013 Nov 4:33-69.

Bardhan IR, Thouin MF. Health information technology and its impact on the quality and cost of healthcare delivery. Decision Support Systems. 2013 May 31;55(2):438-49.

Bates DW, Saria S, Ohno-Machado L, Shah A, Escobar G. Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Affairs. 2014 Jul 1;33(7):1123-31.  Accessed at http://coe.neu.edu/healthcare/pdfs/publications/Health%20Aff-2014-Bates_Using%20Predictive%20Analytics.pdf

Bähler C, Huber CA, Brüngger B, Reich O. Multimorbidity, health care utilization and costs in an elderly community-dwelling population: a claims data based observational study. BMC health services research. 2015 Jan 22;15(1):23. Accessed at https://bmchealthservres.biomedcentral.com/articles/10.1186/s12913-015-0698-2

Boivin A, Lehoux P, Lacombe R, Burgers J, Grol R. Involving patients in setting priorities for healthcare improvement: a cluster randomized trial. Implementation Science. 2014 Feb 20;9(1):24.  Accessed at https://implementationscience.biomedcentral.com/articles/10.1186/1748-5908-9-24

Chawla NV, Davis DA. Bringing big data to personalized healthcare: a patient-centered framework. Journal of general internal medicine. 2013 Sep 1;28(3):660-5.

Drummond MF, Sculpher MJ, Claxton K, Stoddart GL, Torrance GW. Methods for the economic evaluation of health care programmes. Oxford university press; 2015 Sep 24.

Fakhimi M, Probert J. Operations research within UK healthcare: a review. Journal of Enterprise Information Management. 2013 Feb 8;26(1/2):21-49.

Gibson OR, Segal L, McDermott RA. A systematic review of evidence on the association between hospitalisation for chronic disease related ambulatory care sensitive conditions and primary health care resourcing. BMC health services research. 2013 Aug 26;13(1):336.  Accessed a http://www.jmir.org/2013/4/e70/

Gliklich RE, Dreyer NA, Leavy MB, editors. Registries for evaluating patient outcomes: a user’s guide. Government Printing Office; 2014 Apr 1.  Accessed at http://www.effectivehealthcare.ahrq.gov/repFiles/DEcIDEs_Registries.html

Goodman RA. Defining and measuring chronic conditions: imperatives for research, policy, program, and practice. Preventing chronic disease. 2013;10. Accessed at https://www.cdc.gov/Pcd/issues/2013/12_0239.htm

Grembowski D, Schaefer J, Johnson KE, Fischer H, Moore SL, Tai-Seale M, Ricciardi R, Fraser JR, Miller D, LeRoy L. A conceptual model of the role of complexity in the care of patients with multiple chronic conditions. Medical care. 2014 Mar 1;52:S7-14.

Gudleski GD, Satchidanand N, Dunlap LJ, Tahiliani V, Li X, Keefer L, Lackner JM, IBSOS Outcome Study Research Group. Predictors of medical and mental health care use in patients with irritable bowel syndrome in the United States. Behaviour Research and Therapy. 2017 Jan 31;88:65-75.

Guest JF, Panca M, Sladkevicius E, Taheri S, Stradling J. Clinical outcomes and cost-effectiveness of continuous positive airway pressure to manage obstructive sleep apnea in patients with type 2 diabetes in the UK. Diabetes Care. 2014 May 1;37(5):1263-71.  Accessed at https://www.researchgate.net/profile/Shahrad_Taheri/publication/261408103_Clinical_Outcomes_and_Cost-effectiveness_of_Continuous_Positive_Airway_Pressure_to_Manage_Obstructive_Sleep_Apnea_in_Patients_With_Type_2_Diabetes_in_the_UK/links/00b7d534638f3a5be6000000/Clinical-Outcomes-and-Cost-effectiveness-of-Continuous-Positive-Airway-Pressure-to-Manage-Obstructive-Sleep-Apnea-in-Patients-With-Type-2-Diabetes-in-the-UK.pdf

Haas LR, Takahashi PY, Shah ND, Stroebel RJ, Bernard ME, Finnie DM, Naessens JM. Risk-stratification methods for identifying patients for care coordination. The American journal of managed care. 2013 Sep;19(9):725-32.

Hong CS, Siegel AL, Ferris TG. Caring for high-need, high-cost patients: what makes for a successful care management program. Issue Brief (Commonw Fund). 2014 Aug;19(1):9. Accessed at https://www.communitycarenc.org/elements/media/publications/caring-for-high-need-high-cost-patients-what-makes.pdf

Lakshmi C, Iyer SA. Application of queueing theory in health care: A literature review. Operations research for health care. 2013 Jun 30;2(1):25-39.

Ozok AA, Wu H, Garrido M, Pronovost PJ, Gurses AP. Usability and perceived usefulness of personal health records for preventive health care: A case study focusing on patients’ and primary care providers’ perspectives. Applied ergonomics. 2014 May 31;45(3):613-28.

Quaglini S. Compliance with clinical practice guidelines. Stud Health Technol Inform. 2008 Jul 24;139:160-79.

Reed M, Huang J, Brand R, Graetz I, Neugebauer R, Fireman B, Jaffe M, Ballard DW, Hsu J. Implementation of an outpatient electronic health record and emergency department visits, hospitalizations, and office visits among patients with diabetes. Jama. 2013 Sep 11;310(10):1060-5. Accessed at http://jamanetwork.com/journals/jama/fullarticle/1737043

Reeves S, Perrier L, Goldman J, Freeth D, Zwarenstein M. Interprofessional education: effects on professional practice and healthcare outcomes (update). Cochrane Database Syst Rev. 2013 Mar 28;3(3).

Roski J, Bo-Linn GW, Andrews TA. Creating value in health care through big data: opportunities and policy implications. Health Affairs. 2014 Jul 1;33(7):1115-22.

Street RL. How clinician–patient communication contributes to health improvement: modeling pathways from talk to outcome. Patient education and counseling. 2013 Sep 30;92(3):286-91.

Process Mining

Antonelli D, Bruno G. Application of process mining and semantic structuring towards a lean healthcare network. In Working Conference on Virtual Enterprises 2015 Oct 5 (pp. 497-508). Springer International Publishing.

Boere JJ. An analysis and redesign of the ICU weaning process using data analysis and process mining (Doctoral dissertation, Maastricht University Medical Centre). Accessed at https://pure.tue.nl/ws/files/46940995/760532-1.pdf

Bohada JA, Riaño D, López-Vallverdú JA. Automatic generation of clinical algorithms within the state-decision-action model. Expert Systems with Applications. 2012 Sep 15;39(12):10709-21.

Bose RJ, van der Aalst WM. Analysis of Patient Treatment Procedures. In Business Process Management Workshops (1) 2011 Aug 29 (Vol. 99, pp. 165-166). Accessed at http://bpmcenter.org/wp-content/uploads/reports/2011/BPM-11-18.pdf

Bose RJ, van der Aalst WM. Process diagnostics using trace alignment: opportunities, issues, and challenges. Information Systems. 2012 Apr 30;37(2):117-41. Accessed at http://wwwis.win.tue.nl/~wvdaalst/publications/p663.pdf

Bose RJ, van der Aalst WM. When process mining meets bioinformatics. In Forum at the Conference on Advanced Information Systems Engineering (CAiSE) 2011 Jun 20 (pp. 202-217). Springer Berlin Heidelberg. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.298.6878&rep=rep1&type=pdf

Bozkaya M, Gabriels J, van der Werf JM. Process diagnostics: a method based on process mining. In Information, Process, and Knowledge Management, 2009. eKNOW’09. International Conference on 2009 Feb 1 (pp. 22-27). IEEE.  Accessed at http://tmpmining.win.tue.nl/_media/publications/bozkayamethod.pdf

Carbonell Gutiérrez A. Analysis of the treatment of pain and anxiety in the anesthesia care in an ERCP: Process Mining application in Heath Care. [Thesis].  Accessed at https://upcommons.upc.edu/bitstream/handle/2099.1/18067/MasterThesisFinal.pdf

Caron F, Vanthienen J, Vanhaecht K, Van Limbergen E, Deweerdt J, Baesens B. A process mining-based investigation of adverse events in care processes. Health Information Management Journal. 2014 Mar;43(1):16-25. Accessed at http://himaa2.org.au/HIMJ/sites/default/files/HIMJ1307Caron_0.pdf

Caron F, Vanthienen J, De Weerdt J, Baesens B. Advanced care-flow mining and analysis. In International Conference on Business Process Management 2011 Aug 29 (pp. 167-168). Springer Berlin Heidelberg.

Chabrol M, Dalmas B, Norre S, Rodier S. A process tree-based algorithm for the detection of implicit dependencies. In Research Challenges in Information Science (RCIS), 2016 IEEE Tenth International Conference on 2016 Jun 1 (pp. 1-11). IEEE. Accessed at https://www.researchgate.net/profile/Benjamin_Dalmas/publication/304772059_A_Process_Tree-Based_Algorithm_for_the_Detection_of_Implicit_Dependencies/links/577a247e08ae4645d6129d68.pdf

Cho M, Song M, Yoo S. A systematic methodology for outpatient process analysis based on process mining. In Asia-Pacific Conference on Business Process Management 2014 Jul 3 (pp. 31-42). Springer International Publishing.  Accessed at https://www.researchgate.net/profile/Minseok_Song2/publication/296742824_A_Systematic_Methodology_for_Outpatient_Process_Analysis_Based_on_Process_Mining/links/56f5d6c108ae7c1fda2eec22.pdf

Dagliati A, Sacchi L, Cerra C, Leporati P, De Cata P, Chiovato L, Holmes JH, Bellazzi R. Temporal data mining and process mining techniques to identify cardiovascular risk-associated clinical pathways in Type 2 diabetes patients. In Biomedical and Health Informatics (BHI), 2014 IEEE-EMBS International Conference on 2014 Jun 1 (pp. 240-243). IEEE.

De Weerdt J, Caron F, Vanthienen J, Baesens B. Getting a grasp on clinical pathway data: An approach based on process mining. In Pacific-Asia Conference on Knowledge Discovery and Data Mining 2012 May 29 (pp. 22-35). Springer Berlin Heidelberg.

Detro SP, Santos EA, Deschamps F, Vieira AD, Ioshii SO. PROCESS MINING IN HEALTHCARE INSURANCE COMPANIES: IDENTIFYING GUIDELINES AND PRACTICES. In Proceedings of the International Annual Conference of the American Society for Engineering Management. 2014 Jan 1 (p. 1).

Fei H, Meskens N. Discovering patient care process models from event logs. In8th International 530 Conference of Modeling and Simulation, MOSIM 2008 (pp. 10-12). Accessed at http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.167.1391&rep=rep1&type=pdf

Forsberg D, Rosipko B, Sunshine JL. Analyzing pacs usage patterns by means of process mining: Steps toward a more detailed workflow analysis in radiology. Journal of digital imaging. 2016 Feb 1;29(1):47-58.

Garg N, Agarwal S. Process Mining for Clinical Workflows. In Proceedings of the International Conference on Advances in Information Communication Technology & Computing 2016 Aug 12 (p. 5). ACM.

Gell G, Gitter T. Hospital Information System/Electronic Health Record (HIS/HER) and clinical research. In Digital Excellence 2008 (pp. 137-146). Springer Berlin Heidelberg.

Hall R, Belson D, Murali P, Dessouky M. Modeling patient flows through the health care system. In Patient Flow 2013 (pp. 3-42). Springer US.  Accessed at http://ie.technion.ac.il/serveng/Lectures/Hall_Flows_Hospitals_chapter1text.pdf

Han B, Jiang L, Cai H. Abnormal process instances identification method in healthcare environment. In Trust, Security and Privacy in Computing and Communications (TrustCom), 2011 IEEE 10th International Conference on 2011 Nov 16 (pp. 1387-1392). IEEE.

Helm E, Paster F. First steps towards process mining in distributed health information systems. International Journal of Electronics and Telecommunications. 2015 Jun 1;61(2):137-42. Accessed at https://www.degruyter.com/downloadpdf/j/eletel.2015.61.issue-2/eletel-2015-0017/eletel-2015-0017.pdf

Helmering P, Harrison P, Iyer V, Kabra A, Slette JV. Process mining of clinical workflows for quality and process improvement. In HIMSS conference proceedings 2012.

Homayounfar P. Process mining challenges in hospital information systems. In Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on 2012 Sep 9 (pp. 1135-1140). IEEE. Accessed at https://pdfs.semanticscholar.org/e0bf/c6d3e3b4032b51277e737ecd3e9ea122d674.pdf

*Hripcsak G, Albers DJ. Next-generation phenotyping of electronic health records. Journal of the American Medical Informatics Association. 2013 Jan 1;20(1):117-21. Accessed at http://web5.cs.columbia.edu/~julia/papers/2012_JAMIA_Hripcsak_nextGenPhenotype.pdf

Huang Z, Dong W, Ji L, Gan C, Lu X, Duan H. Discovery of clinical pathway patterns from event logs using probabilistic topic models. Journal of biomedical informatics. 2014 Feb 28;47:39-57.  Accessed at http://www.sciencedirect.com/science/article/pii/S1532046413001445

Hussey PS, Schneider EC, Rudin RS, Fox DS, Lai J, Pollack CE. Continuity and the costs of care for chronic disease. JAMA internal medicine. 2014 May 1;174(5):742-8.  Accessed at http://jamanetwork.com/journals/jamainternalmedicine/fullarticle/1835350

Hussey PS, Wertheimer S, Mehrotra A. The association between health care quality and cost: a systematic review. Annals of internal medicine. 2013 Jan 1;158(1):27-34. Accessed at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4863949/

Kaymak U, Mans R, van de Steeg T, Dierks M. On process mining in health care. In Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on 2012 Oct 14 (pp. 1859-1864). IEEE.

Konrad R, DeSotto K, Grocela A, McAuley P, Wang J, Lyons J, Bruin M. Modeling the impact of changing patient flow processes in an emergency department: Insights from a computer simulation study. Operations Research for Health Care. 2013 Dec 31;2(4):66-74.  Abstract at http://www.sciencedirect.com/science/article/pii/S2211692313000052

Lakshmanan GT, Rozsnyai S, Wang F. Investigating clinical care pathways correlated with outcomes. In Business process management 2013 (pp. 323-338). Springer Berlin Heidelberg.  Accessed at http://ai2-s2-pdfs.s3.amazonaws.com/70ad/cc63759c3cb07ef5b9e02ee918a9d95e6a3a.pdf

LANGab M, Bürkle T, Laumann S, Prokosch HU. Process mining for clinical workflows: challenges and current limitations. In EHealth Beyond the Horizon: Get It There: Proceedings of MIE2008 the XXIst International Congress of the European Federation for Medical Informatics 2008 (p. 229).  Accessed at https://pdfs.semanticscholar.org/cb8d/d9182c568638cff00b7370c6d6edc1b392f3.pdf

Low WZ, van der Aalst WM, ter Hofstede AH, Wynn MT, De Weerdt J. Change visualisation: Analysing the resource and timing differences between two event logs. Information Systems. 2017 Apr 30;65:106-23.  Abstract accessed at http://www.sciencedirect.com/science/article/pii/S0306437915301575

Low WZ, De Weerdt J, Wynn MT, ter Hofstede AH, van der Aalst WM, vanden Broucke SK. Perturbing event logs to identify cost reduction opportunities: A genetic algorithm-based approach. In Evolutionary Computation (CEC), 2014 IEEE Congress on 2014 Jul 6 (pp. 2428-2435). IEEE. Accessed at http://eprints.qut.edu.au/74562/1/CostOptimizationIEEEWCCI(Amendments).pdf

Manninen AI. Applying the Principles of Process Mining to Finnish Healthcare. [Thesis]  Accessed at https://aaltodoc.aalto.fi/bitstream/handle/123456789/3204/urn100198.pdf?sequence=1&isAllowed=y

Mans R, Reijers H, van Genuchten M, Wismeijer D. Mining processes in dentistry. In Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium 2012 Jan 28 (pp. 379-388). ACM.  Accessed at https://pure.uva.nl/ws/files/1824132/122275_375092.pdf

Mans R, Schonenberg H, Leonardi G, Panzarasa S, Cavallini A, Quaglini S, van der AALST W. Process mining techniques: an application to stroke care. Studies in health technology and informatics. 2008 Jan 1;136:573.  Accessed at http://schonenberg.info/files/SHTI136-0573.pdf

Mans RS, Schonenberg MH, Song M, van der Aalst WM, Bakker PJ. Application of process mining in healthcare–a case study in a dutch hospital. In International Joint Conference on Biomedical Engineering Systems and Technologies 2008 Jan 28 (pp. 425-438). Springer Berlin Heidelberg. Accessed at http://wwwis.win.tue.nl/~wvdaalst/publications/p499.pdf

Mans RS, van der Aalst W, Vanwersch RJ. Process mining in healthcare: evaluating and exploiting operational healthcare processes. Heidelberg: Springer; 2015 Mar 12.

Mans RS, van der Aalst WM, Vanwersch RJ. Process mining in healthcare: opportunities beyond the ordinary. BPM reports. 2013 Jan 1;1326. Accessed at https://pure.tue.nl/ws/files/3837290/342176341951553.pdf

Mans RS, van der Aalst WM, Vanwersch RJ, Moleman AJ. Process mining in healthcare: Data challenges when answering frequently posed questions. In Process Support and Knowledge Representation in Health Care 2013 (pp. 140-153). Springer Berlin Heidelberg.

Maruster L, Jorna RJ. From data to knowledge: a method for modeling hospital logistic processes. IEEE Transactions on Information Technology in Biomedicine. 2005 Jun;9(2):248-55.  Accessed at http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.132.6302&rep=rep1&type=pdf

McGregor C, Catley C, James A. A process mining driven framework for clinical guideline improvement in critical care. In Proceedings of the Learning from Medical Data Streams Workshop. Bled, Slovenia (July 2011) 2011 Jul. Accessed at https://www.researchgate.net/profile/Pedro_Rodrigues13/publication/236007639_Learning_from_Medical_Data_Streams/links/54e283eb0cf2c3e7d2d3a5e3.pdf#page=34

Molodchenkov A, Khachumov M. Using the DTW method for estimation of deviation of care processes from a care plan. Accessed at https://pdfs.semanticscholar.org/d29b/8d3de1e2dd7737a29f8d39a2f631d9f4bcc5.pdf

Montani S, Leonardi G, Quaglini S, Cavallini A, Micieli G. Improving structural medical process comparison by exploiting domain knowledge and mined information. Artificial intelligence in medicine. 2014 Sep 30;62(1):33-45.  Accessed at http://people.unipmn.it/stefania/papers-pdf/A38.pdf

Montani S, Leonardi G, Quaglini S, Cavallini A, Micieli G. Mining and retrieving medical processes to assess the quality of care. In International Conference on Case-Based Reasoning 2013 Jul 8 (pp. 233-240). Springer Berlin Heidelberg. Accessed at http://people.unipmn.it/stefania/papers-pdf/C98.pdf

Netjes M, Mans RS, Reijers HA, van der Aalst WM, Vanwersch RJ. BPR best practices for the healthcare domain. In International Conference on Business Process Management 2009 Sep 7 (pp. 605-616). Springer Berlin Heidelberg.  Accessed at https://pure.tue.nl/ws/files/3121472/Metis237259.pdf

Nyweide DJ, Anthony DL, Bynum JP, Strawderman RL, Weeks WB, Casalino LP, Fisher ES. Continuity of care and the risk of preventable hospitalization in older adults. JAMA internal medicine. 2013 Nov 11;173(20):1879-85. Accessed at http://jamanetwork.com/journals/jamainternalmedicine/fullarticle/1738715

Ordon M, Urbach D, Mamdani M, Saskin R, Honey RJ, Pace KT. The surgical management of kidney stone disease: a population based time series analysis. The Journal of urology. 2014 Nov 30;192(5):1450-6.

Partington A, Wynn M, Suriadi S, Ouyang C, Karnon J. Process mining for clinical processes: a comparative analysis of four Australian hospitals. ACM Transactions on Management Information Systems (TMIS). 2015 Mar 21;5(4):19. Accessed at http://eprints.qut.edu.au/66728/4/66728.pdf

Partridge AH, Seah DS, King T, Leighl NB, Hauke R, Wollins DS, Von Roenn JH. Developing a service model that integrates palliative care throughout cancer care: the time is now. Journal of Clinical Oncology. 2014 Sep 8;32(29):3330-6.  Accessed at http://ascopubs.org/doi/full/10.1200/jco.2013.54.8149

Peleg M, Soffer P, Ghattas J. Mining process execution and outcomes–Position paper. In International Conference on Business Process Management 2007 Sep 24 (pp. 395-400). Springer Berlin Heidelberg. Accessed at http://s3.amazonaws.com/academia.edu.documents/30976624/Mining_Process_Execution_and_Outcomes_-_Position_Paper.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1487612509&Signature=o9lzM15ux6E1ul8%2BcbuYSwU12fI%3D&response-content-disposition=inline%3B%20filename%3DMining_process_execution_and_outcomes_Po.pdf

Perimal-Lewis L, De Vries D, Thompson CH. Health intelligence: Discovering the process model using process mining by constructing Start-to-End patient journeys. In Proceedings of the Seventh Australasian Workshop on Health Informatics and Knowledge Management-Volume 153 2014 Jan 20 (pp. 59-67). Australian Computer Society, Inc.. Accessed at http://dspace2.flinders.edu.au/xmlui/bitstream/handle/2328/35434/Perimal-Lewis_Health_P2014.pdf?sequence=1

Raghupathi W. Data mining in healthcare. Healthcare Informatics: Improving Efficiency through Technology, Analytics, and Management. 2016 Apr 27:353-72.

Ramos LT. Healthcare Process Analysis: validation and improvements of a data-based method using process mining and visual analytics. Eindhoven University of Technology, Eindhoven. 2009.

Rebuge Á, Ferreira DR. Business process analysis in healthcare environments: A methodology based on process mining. Information systems. 2012 Apr 30;37(2):99-116. Accessed at http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.357.2924&rep=rep1&type=pdf

Rebuge Á, Lapão LV, Freitas A, Cruz-Correia R. A process mining analysis on a Virtual Electronic Patient Record system. In Computer-Based Medical Systems (CBMS), 2013 IEEE 26th International Symposium on 2013 Jun 20 (pp. 554-555). IEEE. Accessed at https://www.researchgate.net/profile/Ricardo_Cruz-Correia/publication/261352150_A_process_mining_analysis_on_a_Virtual_Electronic_Patient_Record_system/links/54b7ab4f0cf24eb34f6eca9e.pdf

Rojas E, Arias M, Sepúlveda M. Clinical processes and its data, what can we do with them. In International Conference on Health Informatics (HEALTHINF) 2015 (pp. 642-647). Accessed at https://www.researchgate.net/profile/Eric_Rojas/publication/277132870_Clinical_Processes_and_Its_Data_What_Can_We_Do_with_Them/links/55c0ec4808ae092e9668360c.pdf

**Rojas E, Munoz-Gama J, Sepúlveda M, Capurro D. Process mining in healthcare: A literature review. Journal of biomedical informatics. 2016 Jun 30;61:224-36.  https://www.researchgate.net/profile/Jorge_Munoz-Gama/publication/301643279_Process_Mining_in_Healthcare_A_literature_review/links/5727d22408ae586b21e2995e.pdf

Safavi KC, Li SX, Dharmarajan K, Venkatesh AK, Strait KM, Lin H, Lowe TJ, Fazel R, Nallamothu BK, Krumholz HM. Hospital variation in the use of noninvasive cardiac imaging and its association with downstream testing, interventions, and outcomes. JAMA internal medicine. 2014 Apr 1;174(4):546-53.  Accessed at http://jamanetwork.com/journals/jamainternalmedicine/fullarticle/1828745?tab=cme

Saultz JW, Jones SM, McDaniel SH, Bagley B, McCormally T, Marker JE, Weida JA, Green LA. A new foundation for the delivery and financing of American health care. Fam Med. 2015 Sep 1;47(8):612-9.  Accessed at https://inside.fammed.wisc.edu/sites/default/files/Delivery%20Financing%20Health%20Care.pdf

Sauver JL, Warner DO, Yawn BP, Jacobson DJ, McGree ME, Pankratz JJ, Melton LJ, Roger VL, Ebbert JO, Rocca WA. Why patients visit their doctors: assessing the most prevalent conditions in a defined American population. In Mayo Clinic Proceedings 2013 Jan 31 (Vol. 88, No. 1, pp. 56-67). Elsevier. Accessed at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3564521/

Shortell SM, Poon BY, Ramsay PP, Rodriguez HP, Ivey SL, Huber T, Rich J, Summerfelt T. A multilevel analysis of patient engagement and patient-reported outcomes in primary care practices of accountable care organizations. Journal of General Internal Medicine. 2017 Feb 3:1-8.

Song M, Günther CW, Van der Aalst WM. Trace clustering in process mining. In International Conference on Business Process Management 2008 Sep 1 (pp. 109-120). Springer Berlin Heidelberg. Accessed at http://ai2-s2-pdfs.s3.amazonaws.com/4211/ea7a87faec1621b4f6b883473b341f75db32.pdf

Suriadi S, Mans RS, Wynn MT, Partington A, Karnon J. Measuring patient flow variations: A cross-organisational process mining approach. In Asia-Pacific Conference on Business Process Management 2014 Jul 3 (pp. 43-58). Springer International Publishing. Accessed at https://www.researchgate.net/profile/Jonathan_Karnon/publication/279886746_Measuring_Patient_Flow_Variations_A_Cross-Organisational_Process_Mining_Approach/links/567865f708ae502c99d56d85.pdf

van der Aalst WM, Low WZ, Wynn MT, ter Hofstede AH. Change your history: Learning from event logs to improve processes. InComputer Supported Cooperative Work in Design (CSCWD), 2015 IEEE 19th International Conference on 2015 May 6 (pp. 7-12). IEEE.  Accessed at http://wwwis.win.tue.nl/~wvdaalst/publications/p821.pdf

van Eck, M.L., 2013. Timestamps Within Healthcare Process Mining Logs (Doctoral dissertation, Master’s thesis, Eindhoven University of Technology, Eindhoven).

Vanwersch RJ, Shahzad K, Vanderfeesten I, Vanhaecht K, Grefen P, Pintelon L, Mendling J, van Merode GG, Reijers HA. A critical evaluation and framework of business process improvement methods. Business & Information Systems Engineering. 2016 Feb 1;58(1):43-53.

Visscher S, Lucas P, Flesch I, Schurink K. Using temporal context-specific independence information in the exploratory analysis of disease processes. In Conference on Artificial Intelligence in Medicine in Europe 2007 Jul 7 (pp. 87-96). Springer Berlin Heidelberg.  Accessed at https://www.researchgate.net/profile/Peter_J_Lucas/publication/221450557_Using_Temporal_Context-Specific_Independence_Information_in_the_Exploratory_Analysis_of_Disease_Processes/links/02bfe511f9b336e150000000.pdf

Wang X, Sontag D, Wang F. Unsupervised learning of disease progression models. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining 2014 Aug 24 (pp. 85-94). ACM.

Warsof SL, Larion S, Abuhamad AZ. Overview of the impact of noninvasive prenatal testing on diagnostic procedures. Prenatal diagnosis. 2015 Oct 1;35(10):972-9.  Accessed at http://www.amfmm.com/Portals/0/Documents/2015-Impact-of-NIPT-Dx-procedures.pdf

Webster C, MSIE M. EHR business process management: from process mining to process improvement to process usability. In Proc Healthcare Systems Process Improvement Conference 2012. Accessed at https://pdfs.semanticscholar.org/3352/da1836a8cfee96631920ba581e1fd65ec764.pdf

Yang W, Su Q. Process mining for clinical pathway: Literature review and future directions. In Service Systems and Service Management (ICSSSM), 2014 11th International Conference on 2014 Jun 25 (pp. 1-5). IEEE.

Zhou Z, Wang Y, Li L. Process mining based modeling and analysis of workflows in clinical care-a case study in a Chicago outpatient clinic. In Networking, Sensing and Control (ICNSC), 2014 IEEE 11th International Conference on 2014 Apr 7 (pp. 590-595). IEEE.

Visual Analytics

Basole RC, Braunstein ML, Kumar V, Park H, Kahng M, Chau DH, Tamersoy A, Hirsh DA, Serban N, Bost J, Lesnick B. Understanding variations in pediatric asthma care processes in the emergency department using visual analytics. Journal of the American Medical Informatics Association. 2015 Mar 1;22(2):318-23.  Accessed at https://pdfs.semanticscholar.org/a128/15095c8e5273d3013e3ae424f2a5482293c8.pdf

Canavan C, West J, Card T. Review article: the economic impact of the irritable bowel syndrome. Alimentary pharmacology & therapeutics. 2014 Nov 1;40(9):1023-34.  Accessed at http://onlinelibrary.wiley.com/doi/10.1111/apt.12938/full

Caron F, Vanthienen J, Vanhaecht K, Van Limbergen E, De Weerdt J, Baesens B. Monitoring care processes in the gynecologic oncology department. Computers in biology and medicine. 2014 Jan 1;44:88-96.

Claes J, Vanderfeesten I, Pinggera J, Reijers HA, Weber B, Poels G. A visual analysis of the process of process modeling. Information Systems and e-Business Management. 2015 Feb 1;13(1):147-90.  Accessed at https://arxiv.org/ftp/arxiv/papers/1511/1511.04055.pdf

De Leoni M, Suriadi S, Ter Hofstede AH, van der Aalst WM. Turning event logs into process movies: animating what has really happened. Software & Systems Modeling. 2016 Jul 1;15(3):707-32.  Accessed at https://www.researchgate.net/profile/Massimiliano_De_Leoni/publication/291748707_Turning_event_logs_into_process_movies_animating_what_has_really_happened/links/5718f7e908aed43f63234d83.pdf

Finkelstein A, Gentzkow M, Williams H. Sources of geographic variation in health care: Evidence from patient migration. The Quarterly Journal of Economics. 2016 Jul 19:qjw023.

Kumar V, Park H, Basole RC, Braunstein M, Kahng M, Chau DH, Tamersoy A, Hirsh DA, Serban N, Bost J, Lesnick B. Exploring clinical care processes using visual and data analytics: challenges and opportunities. InProceedings of the 20th ACM SIGKDD conference on knowledge discovery and data mining workshop on data science for social good 2014.  Accessed at cfvrc

Riemers PP. Process Improvement in Healthcare: a Data-Based Method Using a Combination of Process Mining and Visual Analytics.

 

 

 

 

 

 

 

 

 

 

 


 

Google Scholar Search :

https://scholar.google.com/scholar?start=0&q=healthcare+modelling+phenotypes&hl=en&as_sdt=1,33&as_ylo=2013

GENOMICS

Bermingham ML, Pong-Wong R, Spiliopoulou A, Hayward C, Rudan I, Campbell H, Wright AF, Wilson JF, Agakov F, Navarro P, Haley CS. Application of high-dimensional feature selection: evaluation for genomic prediction in man. Scientific reports. 2015;5.  Accessed at http://pubmedcentralcanada.ca/pmcc/articles/PMC4437376/

Gottesman O, Kuivaniemi H, Tromp G, Faucett WA, Li R, Manolio TA, Sanderson SC, Kannry J, Zinberg R, Basford MA, Brilliant M. The electronic medical records and genomics (eMERGE) network: past, present, and future. Genetics in Medicine. 2013 Jun 6;15(10):761-71.  Accessed at http://www.nature.com/gim/journal/v15/n10/full/gim201372a.html

Hood L, Auffray C. Participatory medicine: a driving force for revolutionizing healthcare. Genome medicine. 2013 Dec 23;5(12):110.  Accessed at https://genomemedicine.biomedcentral.com/articles/10.1186/gm514

Smith ED, Radtke K, Rossi M, Shinde DN, Darabi S, El‐Khechen D, Powis Z, Helbig K, Waller K, Grange DK, Tang S. Classification of Genes: Standardized Clinical Validity Assessment of Gene–Disease Associations Aids Diagnostic Exome Analysis and Reclassifications. Human Mutation. 2017 Feb 1.  Accessed at http://onlinelibrary.wiley.com/doi/10.1002/humu.23183/full

Study TD. Large-scale discovery of novel genetic causes of developmental disorders. Nature. 2015 Mar 12;519(7542):223-8.  Accessed at http://eprints.soton.ac.uk/373093/1/248054_2_merged_1409156614.pdf

PHENOTYPES

Akbaraly T, Sabia S, Hagger-Johnson G, Tabak AG, Shipley MJ, Jokela M, Brunner EJ, Hamer M, Batty GD, Singh-Manoux A, Kivimaki M. Does overall diet in midlife predict future aging phenotypes? A cohort study. The American journal of medicine. 2013 May 31;126(5):411-9.  Accessed at http://www.sciencedirect.com/science/article/pii/S0002934313000806

Bousquet J, Jorgensen C, Dauzat M, Cesario A, Camuzat T, Bourret R, Best N, M Anto J, Abecassis F, Aubas P, Avignon A. Systems medicine approaches for the definition of complex phenotypes in chronic diseases and ageing. From concept to implementation and policies. Current pharmaceutical design. 2014 Nov 1;20(38):5928-44.  Accessed at http://orbilu.uni.lu/bitstream/10993/26640/1/Bousquet%20et%20al%20%20Systems%20Medicine%20Approaches….pdf

Brookes AJ, Robinson PN. Human genotype-phenotype databases: aims, challenges and opportunities. Nature Reviews Genetics. 2015 Nov 10.  Abstract at https://www.ncbi.nlm.nih.gov/pubmed/26553330

Chen Y, Carroll RJ, Hinz ER, Shah A, Eyler AE, Denny JC, Xu H. Applying active learning to high-throughput phenotyping algorithms for electronic health records data. Journal of the American Medical Informatics Association. 2013 Dec 1;20(e2):e253-9.  Accessed at https://www.researchgate.net/profile/Yukun_Chen5/publication/249320257_Applying_active_learning_to_high-throughput_phenotyping_algorithms_for_electronic_health_records_data/links/561288fe08ae4f0b6515866f.pdf

Ho JC, Ghosh J, Sun J. Marble: high-throughput phenotyping from electronic health records via sparse nonnegative tensor factorization. InProceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining 2014 Aug 24 (pp. 115-124). ACM.  Accessed at http://www.sunlab.org/files/2614/0957/7592/MARBLE_tensor_factorization_p115.pdf

Liao WL, Tsai FJ. Personalized medicine: a paradigm shift in healthcare. BioMedicine. 2013 Jun 30;3(2):66-72.  Accessed at http://biomedicine.cmu.edu.tw/doc/7-1.pdf

Liu C, Wang F, Hu J, Xiong H. Temporal phenotyping from longitudinal electronic health records: A graph based framework. InProceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2015 Aug 10 (pp. 705-714). ACM. Accessed at https://pdfs.semanticscholar.org/00e1/7bd820ffdddfeca041f31ae1691d18f6970c.pdf   [TEMPORAL PHENOTYPING]

Pathak J, Bailey KR, Beebe CE, Bethard S, Carrell DS, Chen PJ, Dligach D, Endle CM, Hart LA, Haug PJ, Huff SM. Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium. Journal of the American Medical Informatics Association. 2013 Dec 1;20(e2):e341-8.  Accessed at https://academic.oup.com/jamia/article/20/e2/e341/2909250/Normalization-and-standardization-of-electronic

Pathak J, Kho AN, Denny JC. Electronic health records-driven phenotyping: challenges, recent advances, and perspectives.  JAMIA. Accessed at https://www.researchgate.net/profile/Joshua_Denny/publication/259155540_Electronic_health_records-driven_phenotyping_Challenges_recent_advances_and_perspectives/links/0046352a5096e28098000000.pdf

Richesson RL, Hammond WE, Nahm M, Wixted D, Simon GE, Robinson JG, Bauck AE, Cifelli D, Smerek MM, Dickerson J, Laws RL. Electronic health records based phenotyping in next-generation clinical trials: a perspective from the NIH Health Care Systems Collaboratory. Journal of the American Medical Informatics Association. 2013 Dec 1;20(e2):e226-31.  Accessed at https://academic.oup.com/jamia/article/20/e2/e226/2909215/Electronic-health-records-based-phenotyping-in

Shivade C, Raghavan P, Fosler-Lussier E, Embi PJ, Elhadad N, Johnson SB, Lai AM. A review of approaches to identifying patient phenotype cohorts using electronic health records. Journal of the American Medical Informatics Association. 2014 Mar 1;21(2):221-30. Accessed at https://pdfs.semanticscholar.org/5fd5/85ee0317a34da2df0db80b4b6cd6467c726c.pdf

APPLICATIONS

Translational/Transformational Medicine

Chute CG, Ullman-Cullere M, Wood GM, Lin SM, He M, Pathak J. Some experiences and opportunities for big data in translational research. Genetics in Medicine. 2013 Sep 5;15(10):802-9.  Accessed at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3906918/

Issa NT, Byers SW, Dakshanamurthy S. Big data: the next frontier for innovation in therapeutics and healthcare. Expert review of clinical pharmacology. 2014 May 1;7(3):293-8.  Accessed at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4448933/

Pavel M, Jimison HB, Wactlar HD, Hayes TL, Barkis W, Skapik J, Kaye J. The role of technology and engineering models in transforming healthcare. IEEE reviews in biomedical engineering. 2013;6:156-77. http://ieeexplore.ieee.org/abstract/document/6490450/

Big Data and Cost, Machine Learning

Bates DW, Saria S, Ohno-Machado L, Shah A, Escobar G. Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Affairs. 2014 Jul 1;33(7):1123-31.  Accessed at http://coe.neu.edu/healthcare/pdfs/publications/Health%20Aff-2014-Bates_Using%20Predictive%20Analytics.pdf

Belle A, Thiagarajan R, Soroushmehr SM, Navidi F, Beard DA, Najarian K. Big data analytics in healthcare. BioMed research international. 2015 Jul 2;2015.  Accessed at https://www.hindawi.com/journals/bmri/2015/370194/abs/

Cheng F, Zhao Z. Machine learning-based prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. Journal of the American Medical Informatics Association. 2014 Oct 1;21(e2):e278-86.  Abstract at https://academic.oup.com/jamia/article/21/e2/e278/704905/Machine-learning-based-prediction-of-drug-drug

Murdoch TB, Detsky AS. The inevitable application of big data to health care. Jama. 2013 Apr 3;309(13):1351-2.  Abstract at http://jamanetwork.com/journals/jama/article-abstract/1674245

Behavioral health and Staging Care

Scott J, Leboyer M, Hickie I, Berk M, Kapczinski F, Frank E, Kupfer D, McGorry P. Clinical staging in psychiatry: a cross-cutting model of diagnosis with heuristic and practical value.  Accessed at http://bjp.rcpsych.org/content/202/4/243

DQ & Chronic Disease Management

Liaw ST, Rahimi A, Ray P, Taggart J, Dennis S, de Lusignan S, Jalaludin B, Yeo AE, Talaei-Khoei A. Towards an ontology for data quality in integrated chronic disease management: a realist review of the literature. International journal of medical informatics. 2013 Jan 31;82(1):10-24.  Accessed at https://www.researchgate.net/profile/Siaw-Teng_Liaw/publication/232811250_Towards_an_ontology_for_data_quality_in_integrated_chronic_disease_management_A_realist_review_of_the_literature/links/5689aa5208ae051f9af780b2.pdf

QOC Model

Halfon N, Larson K, Lu M, Tullis E, Russ S. Lifecourse health development: past, present and future. Maternal and child health journal. 2014 Feb 1;18(2):344-65.  Accessed at https://link.springer.com/article/10.1007/s10995-013-1346-2

Personalized Medicine/Healthcare

Andreu-Perez J, Leff DR, Ip HM, Yang GZ. From Wearable Sensors to Smart Implants-–Toward Pervasive and Personalized Healthcare. IEEE Transactions on Biomedical Engineering. 2015 Dec;62(12):2750-62. Accessed at https://www.researchgate.net/profile/Javier_Andreu/publication/275051977_From_Wearable_Sensors_to_Smart_Implants–Toward_Pervasive_and_Personalized_Healthcare/links/55c258a108aebc967defdb1d.pdf

Bielinski SJ, Olson JE, Pathak J, Weinshilboum RM, Wang L, Lyke KJ, Ryu E, Targonski PV, Van Norstrand MD, Hathcock MA, Takahashi PY. Preemptive genotyping for personalized medicine: design of the right drug, right dose, right time—using genomic data to individualize treatment protocol. InMayo Clinic Proceedings 2014 Jan 31 (Vol. 89, No. 1, pp. 25-33). Elsevier.  Accessed at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3932754/

Chawla NV, Davis DA. Bringing big data to personalized healthcare: a patient-centered framework. Journal of general internal medicine. 2013 Sep 1;28(3):660-5.  Accessed at https://link.springer.com/article/10.1007/s11606-013-2455-8

Sagner M, McNeil A, Puska P, Auffray C, Price ND, Hood L, Lavie CJ, Han ZG, Chen Z, Brahmachari SK, McEwen BS. The P4 Health Spectrum–A Predictive, Preventive, Personalized and Participatory Continuum for Promoting Healthspan. Progress in Preventive Medicine. 2017 Jan 1;2(1):e0002.  Accessed at http://journals.lww.com/progprevmed/Abstract/2017/01000/The_P4_Health_Spectrum___A_Predictive,_Preventive,.1.aspx

Viceconti M, Hunter P, Hose R. Big data, big knowledge: big data for personalized healthcare. IEEE journal of biomedical and health informatics. 2015 Jul;19(4):1209-15.  Accessed at http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7047725

Precision Medicine and Phenotyping

Dona AC, Jiménez B, Schäfer H, Humpfer E, Spraul M, Lewis MR, Pearce JT, Holmes E, Lindon JC, Nicholson JK. Precision high-throughput proton NMR spectroscopy of human urine, serum, and plasma for large-scale metabolic phenotyping. Analytical chemistry. 2014 Sep 16;86(19):9887-94.  Accessed at http://s3.amazonaws.com/academia.edu.documents/46229863/Precision_high_throughput_proton_NMR_spe20160604-2488-bg3md2.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1488664007&Signature=WSFUrvOfuS%2FG3bV04AEexR886ww%3D&response-content-disposition=inline%3B%20filename%3DPrecision_high-throughput_proton_NMR_spe.pdf


 

Health Care Processes

Hripcsak G, Albers DJ. Correlating electronic health record concepts with healthcare process events. Journal of the American Medical Informatics Association. 2013 Dec 1;20(e2):e311-8.   Accessed at  http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.958.3170&rep=rep1&type=pdf

Hripcsak G, Albers DJ. Next-generation phenotyping of electronic health records. Journal of the American Medical Informatics Association. 2013 Jan 1;20(1):117-21.  Accessed at http://web5.cs.columbia.edu/~julia/papers/2012_JAMIA_Hripcsak_nextGenPhenotype.pdf        [theoretical, descriptive]

Hripcsak G, Duke JD, Shah NH, Reich CG, Huser V, Schuemie MJ, Suchard MA, Park RW, Wong IC, Rijnbeek PR, van der Lei J. Observational Health Data Sciences and Informatics (OHDSI): opportunities for observational researchers. Studies in health technology and informatics. 2015;216:574.  Accessed at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4815923/

Diagnostic Code methods

Perotte A, Pivovarov R, Natarajan K, Weiskopf N, Wood F, Elhadad N. Diagnosis code assignment: models and evaluation metrics. Journal of the American Medical Informatics Association. 2014 Mar 1;21(2):231-7.  Accessed at https://academic.oup.com/jamia/article/21/2/231/2909245/Diagnosis-code-assignment-models-and-evaluation

MODELLING

Agustí A, Antó JM, Auffray C, Barbé F, Barreiro E, Dorca J, Escarrabill J, Faner R, Furlong LI, Garcia-Aymerich J, Gea J. Personalized respiratory medicine: exploring the horizon, addressing the issues. Summary of a BRN-AJRCCM workshop held in Barcelona on June 12, 2014. American journal of respiratory and critical care medicine. 2015 Feb 15;191(4):391-401.  Accessed at http://www.atsjournals.org/doi/full/10.1164/rccm.201410-1935PP

Angus DC. Fusing randomized trials with big data: the key to self-learning health care systems?. Jama. 2015 Aug 25;314(8):767-8.  Abstract at http://jamanetwork.com/journals/jama/article-abstract/2429723

Boland MR, Hripcsak G, Shen Y, Chung WK, Weng C. Defining a comprehensive verotype using electronic health records for personalized medicine. Journal of the American Medical Informatics Association. 2013 Dec 1;20(e2):e232-8.  Accessed at https://www.researchgate.net/profile/Chunhua_Weng/publication/248383451_A_collaborative_approach_to_developing_an_electronic_health_record_phenotyping_algorithm_for_drug-induced_liver_injury/links/549f0e7e0cf281d393a253a7.pdf

Bowton E, Field JR, Wang S, Schildcrout JS, Van Driest SL, Delaney JT, Cowan J, Weeke P, Mosley JD, Wells QS, Karnes JH. Biobanks and electronic medical records: enabling cost-effective research. Science translational medicine. 2014 Apr 30;6(234):234cm3-. Accessed at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4226414/

Collino S, Martin FP, Rezzi S. Clinical metabolomics paves the way towards future healthcare strategies. British journal of clinical pharmacology. 2013 Mar 1;75(3):619-29.   Accessed at http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2125.2012.04216.x/full

Fernández-Breis JT, Maldonado JA, Marcos M, del Carmen Legaz-García M, Moner D, Torres-Sospedra J, Esteban-Gil A, Martínez-Salvador B, Robles M. Leveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohorts. Journal of the American Medical Informatics Association. 2013 Dec 1;20(e2):e288-96.  Accessed at http://repositori.uji.es/xmlui/bitstream/handle/10234/91153/61764.pdf?sequence=3&isAllowed=y

Gustafsson M, Nestor CE, Zhang H, Barabási AL, Baranzini S, Brunak S, Chung KF, Federoff HJ, Gavin AC, Meehan RR, Picotti P. Modules, networks and systems medicine for understanding disease and aiding diagnosis. Genome medicine. 2014 Oct 17;6(10):82. Accessed at https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-014-0082-6

Mardinoglu A, Nielsen J. New paradigms for metabolic modeling of human cells. Current opinion in biotechnology. 2015 Aug 31;34:91-7.  Accessed at http://s3.amazonaws.com/academia.edu.documents/42025439/New_paradigms_for_metabolic_modeling_of_20160204-30232-fqo5ox.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1488675677&Signature=8aaF2wOKEZ%2FNPOln2NC7zahEyvc%3D&response-content-disposition=inline%3B%20filename%3DNew_paradigms_for_metabolic_modeling_of.pdf

Newby C, Heaney LG, Menzies-Gow A, Niven RM, Mansur A, Bucknall C, Chaudhuri R, Thompson J, Burton P, Brightling C, British Thoracic Society Severe Refractory Asthma Network. Statistical cluster analysis of the British Thoracic Society Severe refractory Asthma Registry: clinical outcomes and phenotype stability. PLoS One. 2014 Jul 24;9(7):e102987.  Accessed at http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0102987

Post AR, Kurc T, Cholleti S, Gao J, Lin X, Bornstein W, Cantrell D, Levine D, Hohmann S, Saltz JH. The Analytic Information Warehouse (AIW): A platform for analytics using electronic health record data. Journal of biomedical informatics. 2013 Jun 30;46(3):410-24.  Accessed at http://www.sciencedirect.com/science/article/pii/S153204641300018X

Prado CM, Siervo M, Mire E, Heymsfield SB, Stephan BC, Broyles S, Smith SR, Wells JC, Katzmarzyk PT. A population-based approach to define body-composition phenotypes. The American journal of clinical nutrition. 2014 Jun 1;99(6):1369-77. Accessed at http://ajcn.nutrition.org/content/99/6/1369.long

Population Macrocosm-Protist Microcosm

Altermatt F, Fronhofer EA, Garnier A, Giometto A, Hammes F, Klecka J, Legrand D, Maechler E, Massie TM, Pennekamp F, Plebani M. Big answers from small worlds: a user’s guide for protist microcosms as a model system in ecology and evolution. Methods in Ecology and Evolution. 2015 Feb 1;6(2):218-31.  Accessed at http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12312/full

 

POPULATIONS

Frailty (Ageing)

Blodgett J, Theou O, Kirkland S, Andreou P, Rockwood K. Frailty in NHANES: comparing the frailty index and phenotype. Archives of gerontology and geriatrics. 2015 Jun 30;60(3):464-70.  Accessed at http://www.sciencedirect.com/science/article/pii/S0167494315000242

Buckinx F, Rolland Y, Reginster JY, Ricour C, Petermans J, Bruyère O. Burden of frailty in the elderly population: perspectives for a public health challenge. Archives of Public Health. 2015 Apr 10;73(1):19. Accessed at https://archpublichealth.biomedcentral.com/articles/10.1186/s13690-015-0068-x

Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. The Lancet. 2013 Mar 8;381(9868):752-62. Accessed at http://europepmc.org/articles/pmc4098658

Garre-Olmo J, Calvó-Perxas L, López-Pousa S, de Gracia Blanco M, Vilalta-Franch J. Prevalence of frailty phenotypes and risk of mortality in a community-dwelling elderly cohort. Age and ageing. 2013 Jan 1;42(1):46-51.  Accessed at https://academic.oup.com/ageing/article/42/1/46/25035/Prevalence-of-frailty-phenotypes-and-risk-of

Graham MM, Galbraith PD, O’Neill D, Rolfson DB, Dando C, Norris CM. Frailty and outcome in elderly patients with acute coronary syndrome. Canadian Journal of Cardiology. 2013 Dec 31;29(12):1610-5.  Accessed at http://www.sciencedirect.com/science/article/pii/S0828282X13013718

Handforth C, Clegg A, Young C, Simpkins S, Seymour MT, Selby PJ, Young J. The prevalence and outcomes of frailty in older cancer patients: a systematic review. Annals of oncology. 2015 Jun 1;26(6):1091-101.  Accessed at https://academic.oup.com/annonc/article/26/6/1091/161199/The-prevalence-and-outcomes-of-frailty-in-older

McAdams‐DeMarco MA, Law A, Salter ML, Chow E, Grams M, Walston J, Segev DL. Frailty and early hospital readmission after kidney transplantation. American journal of transplantation. 2013 Aug 1;13(8):2091-5.  Accessed at http://onlinelibrary.wiley.com/doi/10.1111/ajt.12300/full

McNallan SM, Chamberlain AM, Gerber Y, Singh M, Kane RL, Weston SA, Dunlay SM, Jiang R, Roger VL. Measuring frailty in heart failure: a community perspective. American heart journal. 2013 Oct 31;166(4):768-74. Accessed at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3841984/

Shamliyan T, Talley KM, Ramakrishnan R, Kane RL. Association of frailty with survival: a systematic literature review. Ageing research reviews. 2013 Mar 31;12(2):719-36.  http://s3.amazonaws.com/academia.edu.documents/42527448/Association_of_frailty_with_survival_A_s20160209-11604-1669j3u.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1488658081&Signature=hoNVFUtv67ZaHo5V7EyPtVtw3ao%3D&response-content-disposition=inline%3B%20filename%3DAssociation_of_frailty_with_survival_A_s.pdf

Ageing

Akbaraly TN, Hamer M, Ferrie JE, Lowe G, Batty GD, Hagger-Johnson G, Singh-Manoux A, Shipley MJ, Kivimäki M. Chronic inflammation as a determinant of future aging phenotypes. Canadian Medical Association Journal. 2013 Nov 5;185(16):E763-70.  Accessed at https://hal.archives-ouvertes.fr/file/index/docid/881024/filename/Akbaraly_CMAJ_185_E763.pdf


 

PATHOLOGY: Disease and Disorder Applications

Asthma/COPD

Amelink M, Nijs SB, Groot JC, Tilburg PM, Spiegel PI, Krouwels FH, Lutter R, Zwinderman AH, Weersink EJ, Brinke A, Sterk PJ. Three phenotypes of adult‐onset asthma. Allergy. 2013 May 1;68(5):674-80.  Accessed at http://download.e-pubs.nl/134/marijkeamelink.pdf#page=31

Barker BL, Brightling CE. Phenotyping the heterogeneity of chronic obstructive pulmonary disease. Clinical Science. 2013 Mar 1;124(6):371-87. Accessed at http://www.clinsci.org/content/124/6/371

Beigelman A, Bacharier LB. Management of preschool recurrent wheezing and asthma: a phenotype-based approach. Current opinion in allergy and clinical immunology. 2017 Apr 1;17(2):131-8.  Accessed at http://journals.lww.com/co-allergy/Abstract/2017/04000/Management_of_preschool_recurrent_wheezing_and.14.aspx

Belgrave DC, Simpson A, Semic-Jusufagic A, Murray CS, Buchan I, Pickles A, Custovic A. Joint modeling of parentally reported and physician-confirmed wheeze identifies children with persistent troublesome wheezing. Journal of Allergy and Clinical Immunology. 2013 Sep 30;132(3):575-83.  Accessed at http://www.jacionline.org/article/S0091-6749(13)00975-5/abstract

Bochenek G, Kuschill-Dziurda J, Szafraniec K, Plutecka H, Szczeklik A, Nizankowska-Mogilnicka E. Certain subphenotypes of aspirin-exacerbated respiratory disease distinguished by latent class analysis. Journal of Allergy and Clinical Immunology. 2014 Jan 31;133(1):98-103. Abstract at http://www.sciencedirect.com/science/article/pii/S0091674913010622

Boudier A, Curjuric I, Basagaña X, Hazgui H, Anto JM, Bousquet J, Bridevaux PO, Dupuis-Lozeron E, Garcia-Aymerich J, Heinrich J, Janson C. Ten-year follow-up of cluster-based asthma phenotypes in adults. A pooled analysis of three cohorts. American journal of respiratory and critical care medicine. 2013 Sep 1;188(5):550-60. Accessed at http://www.atsjournals.org/doi/full/10.1164/rccm.201301-0156OC

Chapman DG, Irvin CG, Kaminsky DA, Forgione PM, Bates JH, Dixon AE. Influence of distinct asthma phenotypes on lung function following weight loss in the obese. Respirology. 2014 Nov 1;19(8):1170-7. Accessed at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4194162/

Cho MH, Castaldi PJ, Hersh CP, Hobbs BD, Barr RG, Tal-Singer R, Bakke P, Gulsvik A, San José Estépar R, Van Beek EJ, Coxson HO. A genome-wide association study of emphysema and airway quantitative imaging phenotypes. American journal of respiratory and critical care medicine. 2015 Sep 1;192(5):559-69. Accessed at http://www.atsjournals.org/doi/full/10.1164/rccm.201501-0148OC#readcube-epdf

Chung KF. Asthma phenotyping: a necessity for improved therapeutic precision and new targeted therapies. Journal of internal medicine. 2016 Feb 1;279(2):192-204.  Accessed at https://www.researchgate.net/profile/Kian_Fan_Chung/publication/278330273_Asthma_phenotyping_A_necessity_for_improved_therapeutic_precision_and_new_targeted_therapies/links/570f72f608ae38897ba0faa9.pdf

Holguin F, Comhair SA, Hazen SL, Powers RW, Khatri SS, Bleecker ER, Busse WW, Calhoun WJ, Castro M, Fitzpatrick AM, Gaston B. An association between L-arginine/asymmetric dimethyl arginine balance, obesity, and the age of asthma onset phenotype. American journal of respiratory and critical care medicine. 2013 Jan 15;187(2):153-9.  Accessed at http://www.atsjournals.org/doi/full/10.1164/rccm.201207-1270OC

Kupczyk M, Ten Brinke A, Sterk PJ, Bel EH, Papi A, Chanez P, Nizankowska‐Mogilnicka E, Gjomarkaj M, Gaga M, Brusselle G, Dahlén B. Frequent exacerbators–a distinct phenotype of severe asthma. Clinical & Experimental Allergy. 2014 Feb 1;44(2):212-21.  Accessed at https://biblio.ugent.be/publication/5772612/file/5772656.pdf

Meyers DA, Bleecker ER, Holloway JW, Holgate ST. Asthma genetics and personalised medicine. The Lancet Respiratory medicine. 2014 May 31;2(5):405-15. Abstract at http://europepmc.org/articles/pmc4768462

Schatz M, Hsu JW, Zeiger RS, Chen W, Dorenbaum A, Chipps BE, Haselkorn T. Phenotypes determined by cluster analysis in severe or difficult-to-treat asthma. Journal of Allergy and Clinical Immunology. 2014 Jun 30;133(6):1549-56.  Accessed at http://www.jacionline.org/article/S0091-6749(13)01554-6/abstract

Atrial Fibrillation

Kirchhof P, Breithardt G, Bax J, Benninger G, Blomstrom-Lundqvist C, Boriani G, Brandes A, Brown H, Brueckmann M, Calkins H, Calvert M. A roadmap to improve the quality of atrial fibrillation management: proceedings from the fifth Atrial Fibrillation Network/European Heart Rhythm Association consensus conference. Europace. 2016 Jan 1;18(1):37-50.  Accessed at https://academic.oup.com/europace/article/18/1/37/2398833/A-roadmap-to-improve-the-quality-of-atrial

Autism: Social Visual Engagement

Klin A, Shultz S, Jones W. Social visual engagement in infants and toddlers with autism: Early developmental transitions and a model of pathogenesis. Neuroscience & Biobehavioral Reviews. 2015 Mar 31;50:189-203.  Accessed at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4355308/

Lundström S, Reichenberg A, Anckarsäter H, Lichtenstein P, Gillberg C. Autism phenotype versus registered diagnosis in Swedish children: prevalence trends over 10 years in general population samples. bmj. 2015 Apr 28;350:h1961.  Accessed at http://www.bmj.com/content/350/bmj.h1961.short

Behavioral/Mental Health

Fusar-Poli P, Yung AR, McGorry P, Van Os J. Lessons learned from the psychosis high-risk state: towards a general staging model of prodromal intervention. Psychological medicine. 2014;44(01):17-24. Accessed at https://www.researchgate.net/profile/Paolo_Fusar-Poli/publication/235646805_Lessons_learned_from_the_psychosis_high-risk_state_Towards_a_general_staging_model_of_prodromal_intervention/links/0deec530efee1c2236000000.pdf   [Staging model, risk assessment]

Hickie IB, Scott EM, Hermens DF, Naismith SL, Guastella AJ, Kaur M, Sidis A, Whitwell B, Glozier N, Davenport T, Pantelis C. Applying clinical staging to young people who present for mental health care. Early Intervention in Psychiatry. 2013 Feb 1;7(1):31-43. Abstract at http://onlinelibrary.wiley.com/doi/10.1111/j.1751-7893.2012.00366.x/full

McGorry PD. The next stage for diagnosis: validity through utility. World Psychiatry. 2013 Oct 1;12(3):213-5.  Accessed at http://onlinelibrary.wiley.com/doi/10.1002/wps.20080/full

Persico AM, Arango C, Buitelaar JK, Correll CU, Glennon JC, Hoekstra PJ, Moreno C, Vitiello B, Vorstman J, Zuddas A. Unmet needs in paediatric psychopharmacology: present scenario and future perspectives. European Neuropsychopharmacology. 2015 Oct 31;25(10):1513-31. http://speapsl.aphp.fr/pdfpublications/2015/2015-27.pdf

Cancer, Breast

Ashraf AB, Daye D, Gavenonis S, Mies C, Feldman M, Rosen M, Kontos D. Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles. Radiology. 2014 Apr 4;272(2):374-84.  Accessed at http://pubs.rsna.org/doi/full/10.1148/radiol.14131375

Domingo L, Salas D, Zubizarreta R, Baré M, Sarriugarte G, Barata T, Ibáñez J, Blanch J, Puig-Vives M, Fernández AB, Castells X. Tumor phenotype and breast density in distinct categories of interval cancer: results of population-based mammography screening in Spain. Breast Cancer Research. 2014 Jan 10;16(1):R3.  Accessed at https://breast-cancer-research.biomedcentral.com/articles/10.1186/bcr3595   [4 phenotypes]

Lee AJ, Cunningham AP, Kuchenbaecker KB, Mavaddat N, Easton DF, Antoniou AC. BOADICEA breast cancer risk prediction model: updates to cancer incidences, tumour pathology and web interface. British journal of cancer. 2014 Jan 21;110(2):535-45. Accessed at http://www.nature.com/bjc/journal/v110/n2/full/bjc2013730a.html

Moon HG, Han W, Ahn SK, Cho N, Moon WK, Im SA, Park IA, Noh DY. Breast cancer molecular phenotype and the use of HER2-targeted agents influence the accuracy of breast MRI after neoadjuvant chemotherapy. Annals of surgery. 2013 Jan 1;257(1):133-7.

Cancer, Colorectal

Askari A, Nachiappan S, Currie A, Latchford A, Stebbing J, Bottle A, Athanasiou T, Faiz O. The relationship between ethnicity, social deprivation and late presentation of colorectal cancer. Cancer Epidemiology. 2017 Apr 30;47:88-93. Accessed at https://www.researchgate.net/profile/Alan_Askari/publication/313315127_The_relationship_between_ethnicity_social_deprivation_and_late_presentation_of_colorectal_cancer/links/5895ab9492851c8bb677ad55/The-relationship-between-ethnicity-social-deprivation-and-late-presentation-of-colorectal-cancer.pdf

Matano M, Date S, Shimokawa M, Takano A, Fujii M, Ohta Y, Watanabe T, Kanai T, Sato T. Modeling colorectal cancer using CRISPR-Cas9-mediated engineering of human intestinal organoids. Nature medicine. 2015 Mar 1;21(3):256-62.  Accessed at http://www.sonidel.com/NEPA21/NEPA21_Organoid_EP/Modeling_colorectal_cancer_using_CRISPR-Cas9-mediated_engineering_of_human_intestinal_organoids.pdf

Cancer, General

Shaikh AR, Butte AJ, Schully SD, Dalton WS, Khoury MJ, Hesse BW. Collaborative biomedicine in the age of big data: the case of cancer. Journal of medical Internet research. 2014;16(4):e101. Accessed at http://www.jmir.org/2014/4/e101/

Wang Z, Deisboeck TS. Mathematical modeling in cancer drug discovery. Drug discovery today. 2014 Feb 28;19(2):145-50.  Abstract at http://www.sciencedirect.com/science/article/pii/S1359644613002018

Cancer, Leukemia

Evans WE, Crews KR, Pui CH. A healthcare system perspective on implementing genomic medicine: pediatric acute lymphoblastic leukemia as a paradigm. Clinical pharmacology and therapeutics. 2013 Aug;94(2):224.  Accessed at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3720796/

Cancer, Lung

Yamamoto S, Korn RL, Oklu R, Migdal C, Gotway MB, Weiss GJ, Iafrate AJ, Kim DW, Kuo MD. ALK molecular phenotype in non–small cell lung cancer: CT radiogenomic characterization. Radiology. 2014 Jul 24;272(2):568-76.  Accessed at http://pubs.rsna.org/doi/full/10.1148/radiol.14140789    [NSCLC]

Cancer, Melanoma

Usher-Smith JA, Emery J, Kassianos AP, Walter FM. Risk prediction models for melanoma: a systematic review. Cancer Epidemiology and Prevention Biomarkers. 2014 Jun 3:cebp-0295.  Accessed at http://cebp.aacrjournals.org/content/cebp/early/2014/06/03/1055-9965.EPI-14-0295.full.pdf

Cardiology

Ademi Z, Watts GF, Juniper A, Liew D. A systematic review of economic evaluations of the detection and treatment of familial hypercholesterolemia. International journal of cardiology. 2013 Sep 10;167(6):2391-6.  Accessed at https://www.researchgate.net/profile/Zanfina_Ademi/publication/235771213_A_Systematic_Review_of_Economic_Evaluations_of_the_Detection_and_Treatment_of_Familial_Hypercholesterolemia/links/550965e20cf2d7a2812ca197/A-Systematic-Review-of-Economic-Evaluations-of-the-Detection-and-Treatment-of-Familial-Hypercholesterolemia.pdf

Louridas G, Lourida K. The complex cardiac atherosclerotic disorder: The elusive role of genetics and the new consensus of systems biology approach. Journal of Advanced Therapies and Medical Innovation Sciences. 2017 Feb 5;2. Accessed at http://j-atamis.org/index.php/jatamis/article/viewFile/379/J.ATAMIS%202017%3B2-10-17.pdf

Neeland IJ, Drazner MH, Berry JD, Ayers CR, Seliger SL, Nambi V, McGuire DK, Omland T, de Lemos JA. Biomarkers of chronic cardiac injury and hemodynamic stress identify a malignant phenotype of left ventricular hypertrophy in the general population. Journal of the American College of Cardiology. 2013 Jan 15;61(2):187-95.  Accessed at http://www.sciencedirect.com/science/article/pii/S0735109712053028

O’mahony C, Jichi F, Pavlou M, Monserrat L, Anastasakis A, Rapezzi C, Biagini E, Gimeno JR, Limongelli G, McKenna WJ, Omar RZ. A novel clinical risk prediction model for sudden cardiac death in hypertrophic cardiomyopathy (HCM Risk-SCD). European heart journal. 2014 Aug 7;35(30):2010-20.  Accessed at https://academic.oup.com/eurheartj/article/35/30/2010/467191/A-novel-clinical-risk-prediction-model-for-sudden

Chronic Disease

Simmons LA, Wolever RQ, Bechard EM, Snyderman R. Patient engagement as a risk factor in personalized health care: a systematic review of the literature on chronic disease. Genome medicine. 2014 Feb 26;6(2):16.  Accessed at https://genomemedicine.biomedcentral.com/articles/10.1186/gm533

Chronic Pain

Vehof J, Zavos HM, Lachance G, Hammond CJ, Williams FM. Shared genetic factors underlie chronic pain syndromes. PAIN®. 2014 Aug 31;155(8):1562-8. Accessed at http://www.twinsuk.ac.uk/wp-content/uploads/2016/04/Shared-genetic-factors-underlie-chronic-pain-syndromes.pdf

Waxman SG, Merkies IS, Gerrits MM, Dib-Hajj SD, Lauria G, Cox JJ, Wood JN, Woods CG, Drenth JP, Faber CG. Sodium channel genes in pain-related disorders: phenotype–genotype associations and recommendations for clinical use. The Lancet Neurology. 2014 Nov 30;13(11):1152-60.  Accessed at https://pdfs.semanticscholar.org/8298/fe06c9d171b8b36411bc1507d8238e6485a4.pdf

Clostridium difficile (global, microbiomes researchiuuu)

He M, Miyajima F, Roberts P, Ellison L, Pickard DJ, Martin MJ, Connor TR, Harris SR, Fairley D, Bamford KB, D’Arc S. Emergence and global spread of epidemic healthcare-associated Clostridium difficile. Nature genetics. 2013 Jan 1;45(1):109-13.  Accessed at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3605770/

Schubert AM, Rogers MA, Ring C, Mogle J, Petrosino JP, Young VB, Aronoff DM, Schloss PD. Microbiome data distinguish patients with Clostridium difficile infection and non-C. difficile-associated diarrhea from healthy controls. MBio. 2014 Jul 1;5(3):e01021-14.  Accessed at http://mbio.asm.org/content/5/3/e01021-14.long

 

Cystic Fibrosis

Stephenson AL, Tom M, Berthiaume Y, Singer LG, Aaron SD, Whitmore GA, Stanojevic S. A contemporary survival analysis of individuals with cystic fibrosis: a cohort study. European Respiratory Journal. 2015 Mar 1;45(3):670-9. Accessed at http://erj.ersjournals.com/content/45/3/670

Crohn’s Disease

Ellinghaus D, Bethune J, Petersen BS, Franke A. The genetics of Crohn’s disease and ulcerative colitis–status quo and beyond. Scandinavian journal of gastroenterology. 2015 Jan 2;50(1):13-23.  Accessed at http://www.tandfonline.com/doi/full/10.3109/00365521.2014.990507

Mazor Y, Almog R, Kopylov U, Ben Hur D, Blatt A, Dahan A, Waterman M, Ben‐Horin S, Chowers Y. Adalimumab drug and antibody levels as predictors of clinical and laboratory response in patients with Crohn’s disease. Alimentary pharmacology & therapeutics. 2014 Sep 1;40(6):620-8. Accessed at http://onlinelibrary.wiley.com/doi/10.1111/apt.12869/full

Diabetes

Cunha-Vaz J, Ribeiro L, Lobo C. Phenotypes and biomarkers of diabetic retinopathy. Progress in retinal and eye research. 2014 Jul 31;41:90-111. Accessed at http://biomedfrontiers.org/diabetes-obesity-2015-5-25/

Liaw ST, Taggart J, Yu H, de Lusignan S, Kuziemsky C, Hayen A. Integrating electronic health record information to support integrated care: practical application of ontologies to improve the accuracy of diabetes disease registers. Journal of Biomedical Informatics. 2014 Dec 31;52:364-72. Accessed at http://www.sciencedirect.com/science/article/pii/S1532046414001798

Richesson RL, Rusincovitch SA, Wixted D, Batch BC, Feinglos MN, Miranda ML, Hammond WE, Califf RM, Spratt SE. A comparison of phenotype definitions for diabetes mellitus. Journal of the American Medical Informatics Association. 2013 Dec 1;20(e2):e319-26. Accessed at http://s3.amazonaws.com/academia.edu.documents/43661511/Electronic_health_records_based_phenotyp20160312-2545-107nywv.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1488667359&Signature=W3ElPvo1Jxn2DP%2BPeXpUpGC%2BqMU%3D&response-content-disposition=inline%3B%20filename%3DElectronic_health_records_based_phenotyp.pdf

Väremo L, Nookaew I, Nielsen J. Novel insights into obesity and diabetes through genome-scale metabolic modeling. Frontiers in physiology. 2013 Apr 25;4:92.  Accessed at http://journal.frontiersin.org/article/10.3389/fphys.2013.00092/full

Hematological

Westbury SK, Turro E, Greene D, Lentaigne C, Kelly AM, Bariana TK, Simeoni I, Pillois X, Attwood A, Austin S, Jansen SB. Human phenotype ontology annotation and cluster analysis to unravel genetic defects in 707 cases with unexplained bleeding and platelet disorders. Genome medicine. 2015 Apr 9;7(1):36.  Accessed at https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-015-0151-5

Hypercholesterolemia

Ademi Z, Watts GF, Pang J, Sijbrands EJ, van Bockxmeer FM, O’Leary P, Geelhoed E, Liew D. Cascade screening based on genetic testing is cost-effective: evidence for the implementation of models of care for familial hypercholesterolemia. Journal of clinical lipidology. 2014 Aug 31;8(4):390-400. Accessed at https://www.researchgate.net/profile/Zanfina_Ademi/publication/262936638_Cascade_Screening_Based_on_Genetic_Testing_is_Cost-effective_Evidence_for_the_Implementation_of_Models_of_Care_for_Familial_Hypercholesterolaemia/links/5509637b0cf27e990e0e6fad.pdf

Infectious Disease (incl. Spatial approach)

Banerjee R, Johnston B, Lohse C, Porter SB, Clabots C, Johnson JR. Escherichia coli sequence type 131 is a dominant, antimicrobial-resistant clonal group associated with healthcare and elderly hosts. Infection Control & Hospital Epidemiology. 2013 Apr 1;34(04):361-9.  Accessed at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3916146/

Braykov NP, Eber MR, Klein EY, Morgan DJ, Laxminarayan R. Trends in resistance to carbapenems and third-generation cephalosporins among clinical isolates of Klebsiella pneumoniae in the United States, 1999–2010. Infection Control & Hospital Epidemiology. 2013 Mar 1;34(03):259-68.  Accessed at https://www.cambridge.org/core/journals/infection-control-and-hospital-epidemiology/article/div-classtitletrends-in-resistance-to-carbapenems-and-third-generation-cephalosporins-among-clinical-isolates-of-span-classitalicklebsiella-pneumoniaespan-in-the-united-states-19992010div/92079FCA7AE739268B12B1EA67759598

Davis GS, Sevdalis N, Drumright LN. Spatial and temporal analyses to investigate infectious disease transmission within healthcare settings. Journal of Hospital Infection. 2014 Apr 30;86(4):227-43.  Accessed at https://www.researchgate.net/profile/Nick_Sevdalis/publication/260428928_Spatial_and_temporal_analyses_to_investigate_infectious_disease_transmission_within_healthcare_settings/links/02e7e53557e46a114b000000.pdf

McCarthy H, Rudkin JK, Black NS, Gallagher L, O’Neill E, O’Gara JP. Methicillin resistance and the biofilm phenotype in Staphylococcus aureus. Front Cell Infect Microbiol. 2015 Jan 1;5(1).  Accessed at http://journal.frontiersin.org/article/10.3389/fcimb.2015.00001/full

Perfect JR. Cryptococcosis: a model for the understanding of infectious diseases. The Journal of clinical investigation. 2014 May 1;124(5):1893-5.  Accessed at https://www.jci.org/articles/view/75241/version/2/pdf/render

Kidney

Devuyst O, Knoers NV, Remuzzi G, Schaefer F. Rare inherited kidney diseases: challenges, opportunities, and perspectives. The Lancet. 2014 May 30;383(9931):1844-59.  Accessed at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4135047/pdf/nihms603678.pdf

Metabolic Phenotypes

Hwang YC, Hayashi T, Fujimoto WY, Kahn SE, Leonetti DL, McNeely MJ, Boyko EJ. Visceral abdominal fat accumulation predicts the conversion of metabolically healthy obese subjects to an unhealthy phenotype. International journal of obesity (2005). 2015 Sep;39(9):1365.  Accessed at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4564328/

Mardinoglu A, Gatto F, Nielsen J. Genome‐scale modeling of human metabolism–a systems biology approach. Biotechnology journal. 2013 Sep 1;8(9):985-96.  Accessed at https://www.researchgate.net/profile/Adil_Mardinoglu/publication/236277396_Genome-scale_modeling_of_human_metabolism_-_a_systems_biology_approach/links/00b4951acc0588b344000000.pdf

Moco S, Collino S, Rezzi S, Martin FP. Metabolomics perspectives in pediatric research. Pediatric research. 2013 Jan 11;73(4-2):570-6. Accessed at http://www.nature.com/pr/journal/v73/n4-2/full/pr20131a.html

Sinues PM, Kohler M, Zenobi R. Human breath analysis may support the existence of individual metabolic phenotypes. PloS one. 2013 Apr 3;8(4):e59909.  Accessed at http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0059909

Osteopathy

Sinder BP, White LE, Salemi JD, Ominsky MS, Caird MS, Marini JC, Kozloff KM. Adult Brtl/+ mouse model of osteogenesis imperfecta demonstrates anabolic response to sclerostin antibody treatment with increased bone mass and strength. Osteoporosis International. 2014 Aug 1;25(8):2097-107.  Accessed at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4415164/

Parkinsonism

Fereshtehnejad SM, Romenets SR, Anang JB, Latreille V, Gagnon JF, Postuma RB. New clinical subtypes of Parkinson disease and their longitudinal progression: a prospective cohort comparison with other phenotypes. JAMA neurology. 2015 Aug 1;72(8):863-73.  Accessed at http://jamanetwork.com/journals/jamaneurology/fullarticle/2318972

Janezic S, Threlfell S, Dodson PD, Dowie MJ, Taylor TN, Potgieter D, Parkkinen L, Senior SL, Anwar S, Ryan B, Deltheil T. Deficits in dopaminergic transmission precede neuron loss and dysfunction in a new Parkinson model. Proceedings of the National Academy of Sciences. 2013 Oct 15;110(42):E4016-25.  Accessed at http://www.pnas.org/content/110/42/E4016.full

Pharmacogenomics

Bell GC, Crews KR, Wilkinson MR, Haidar CE, Hicks JK, Baker DK, Kornegay NM, Yang W, Cross SJ, Howard SC, Freimuth RR. Development and use of active clinical decision support for preemptive pharmacogenomics. Journal of the American Medical Informatics Association. 2014 Feb 1;21(e1):e93-9.  Accessed at https://pdfs.semanticscholar.org/a8fc/102afcf65d18bee3d7a688f705b804b10f64.pdf

Hoffman JM, Haidar CE, Wilkinson MR, Crews KR, Baker DK, Kornegay NM, Yang W, Pui CH, Reiss UM, Gaur AH, Howard SC. PG4KDS: A model for the clinical implementation of pre‐emptive pharmacogenetics. InAmerican Journal of Medical Genetics Part C: Seminars in Medical Genetics 2014 Mar 1 (Vol. 166, No. 1, pp. 45-55). Accessed at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4056586/

Pneumonia (Aspiration)

Taylor JK, Fleming GB, Singanayagam A, Hill AT, Chalmers JD. Risk factors for aspiration in community-acquired pneumonia: analysis of a hospitalized UK cohort. The American journal of medicine. 2013 Nov 30;126(11):995-1001.  Accessed at https://www.researchgate.net/profile/Joanne_Taylor9/publication/256928041_Risk_Factors_for_Aspiration_in_Community-acquired_Pneumonia_Analysis_of_a_Hospitalized_UK_Cohort/links/56b350ba08ae156bc5fb19b9.pdf

PTSD (see also Behavioral Health)

Armour C, Tsai J, Durham TA, Charak R, Biehn TL, Elhai JD, Pietrzak RH. Dimensional structure of DSM-5 posttraumatic stress symptoms: Support for a hybrid Anhedonia and Externalizing Behaviors model. Journal of Psychiatric Research. 2015 Feb 28;61:106-13. Accessed at https://www.researchgate.net/profile/Jon_Elhai/publication/268694572_Dimensional_structure_of_DSM-5_posttraumatic_stress_symptoms_Support_for_a_hybrid_Anhedonia_and_Externalizing_Behaviors_model/links/548a67e80cf2d1800d7aab31.pdf

Brown GR, Jones KT. Mental health and medical health disparities in 5135 transgender veterans receiving healthcare in the Veterans Health Administration: a case–control study. LGBT health. 2016 Apr 1;3(2):122-31.  Abstract at http://online.liebertpub.com/doi/abs/10.1089/lgbt.2015.0058  [Transgender]

Harpaz-Rotem I, Tsai J, Pietrzak RH, Hoff R. The dimensional structure of posttraumatic stress symptomatology in 323,903 US veterans. Journal of Psychiatric Research. 2014 Feb 28;49:31-6. Abstract at http://www.sciencedirect.com/science/article/pii/S0022395613003373

Pietrzak RH, Tsai J, Armour C, Mota N, Harpaz-Rotem I, Southwick SM. Functional significance of a novel 7-factor model of DSM-5 PTSD symptoms: Results from the National Health and Resilience in Veterans Study. Journal of Affective Disorders. 2015 Mar 15;174:522-6. Accessed at https://www.researchgate.net/profile/Cherie_Armour2/publication/270513540_Functional_significance_of_a_novel_7-factor_model_of_DSM-5_PTSD_symptoms_Results_from_the_National_Health_and_Resilience_in_Veterans_Study/links/5523ec3a0cf223eed3807769.pdf

Rheumatology

Eleftheriou D, Isenberg DA, Wedderburn LR, Ioannou Y. The coming of age of adolescent rheumatology. Nature Reviews Rheumatology. 2014 Mar 1;10(3):187-93.  Abstract at https://www.ncbi.nlm.nih.gov/pubmed/24394351

Schizophrenia

Bohlken MM, Brouwer RM, Mandl RC, Van den Heuvel MP, Hedman AM, De Hert M, Cahn W, Kahn RS, Pol HE. Structural brain connectivity as a genetic marker for schizophrenia. JAMA psychiatry. 2016 Jan 1;73(1):11-9.  Accessed at https://pdfs.semanticscholar.org/c445/0cb829e1de53fe0eed9b8b83ccb73afd85c9.pdf

Lieberman JA, Dixon LB, Goldman HH. Early detection and intervention in schizophrenia: a new therapeutic model. Jama. 2013 Aug 21;310(7):689-90.  Abstract at http://jamanetwork.com/journals/jama/article-abstract/1730521

Light G, Greenwood TA, Swerdlow NR, Calkins ME, Freedman R, Green MF, Gur RE, Gur RC, Lazzeroni LC, Nuechterlein KH, Olincy A. Comparison of the heritability of schizophrenia and endophenotypes in the COGS-1 family study. Schizophrenia bulletin. 2014 Jun 5:sbu064.  Accessed at https://academic.oup.com/schizophreniabulletin/article/40/6/1404/2886813/Comparison-of-the-Heritability-of-Schizophrenia

Takao K, Kobayashi K, Hagihara H, Ohira K, Shoji H, Hattori S, Koshimizu H, Umemori J, Toyama K, Nakamura HK, Kuroiwa M. Deficiency of schnurri-2, an MHC enhancer binding protein, induces mild chronic inflammation in the brain and confers molecular, neuronal, and behavioral phenotypes related to schizophrenia. Neuropsychopharmacology. 2013 Jul 1;38(8):1409-25.  Accessed at http://www.nature.com/npp/journal/v38/n8/full/npp201338a.html

Wen Z, Nguyen HN, Guo Z, Lalli MA, Wang X, Su Y, Kim NS, Yoon KJ, Shin J, Zhang C, Makri G. Synaptic dysregulation in a human iPS cell model of mental disorders. Nature. 2014 Nov 20;515(7527):414-8.  Accessed at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4501856/

Sickle Cell

Connes P, Lamarre Y, Waltz X, Ballas SK, Lemonne N, Etienne‐Julan M, Hue O, Hardy‐Dessources MD, Romana M. Haemolysis and abnormal haemorheology in sickle cell anaemia. British journal of haematology. 2014 May 1;165(4):564-72. Accessed at http://onlinelibrary.wiley.com/doi/10.1111/bjh.12786/full

Hsieh MM, Fitzhugh CD, Weitzel RP, Link ME, Coles WA, Zhao X, Rodgers GP, Powell JD, Tisdale JF. Nonmyeloablative HLA-matched sibling allogeneic hematopoietic stem cell transplantation for severe sickle cell phenotype. Jama. 2014 Jul 2;312(1):48-56.  Accessed at http://jamanetwork.com/journals/jama/fullarticle/1884578

Sleep Apnea

Kheirandish-Gozal L, Gozal D. Genotype–phenotype interactions in pediatric obstructive sleep apnea. Respiratory physiology & neurobiology. 2013 Nov 1;189(2):338-43.  Accessed at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3751986/pdf/nihms463971.pdf

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