Next-generation phenotyping of electronic health records

The national adoption of electronic health records (EHR) promises to make an unprecedented amount of data available for clinical research, but the data are complex, inaccurate, and frequently missing, and the record reflects complex processes aside from the patient's physiological state. We believe that the path forward requires studying the EHR as an object of interest in itself, and that new models, learning from data, and collaboration will lead to efficient use of the valuable information currently locked in health records.

[1]  George Hripcsak,et al.  Research Paper: The Role of Domain Knowledge in Automating Medical Text Report Classification , 2003, J. Am. Medical Informatics Assoc..

[2]  George Hripcsak,et al.  Temporal reasoning with medical data - A review with emphasis on medical natural language processing , 2007, J. Biomed. Informatics.

[3]  Donald C. Trost,et al.  Information mining over heterogeneous and high-dimensional time-series data in clinical trials databases , 2006, IEEE Transactions on Information Technology in Biomedicine.

[4]  Peter Szolovits,et al.  Genetic basis of autoantibody positive and negative rheumatoid arthritis risk in a multi-ethnic cohort derived from electronic health records. , 2011, American journal of human genetics.

[5]  M. Fine,et al.  A prediction rule to identify low-risk patients with community-acquired pneumonia. , 1997, The New England journal of medicine.

[6]  G Hripcsak,et al.  Natural language processing and its future in medicine. , 1999, Academic medicine : journal of the Association of American Medical Colleges.

[7]  Stijn Heymans,et al.  Semantic validation of the use of SNOMED CT in HL7 clinical documents , 2011, J. Biomed. Semant..

[8]  Jin Fan,et al.  Leveraging informatics for genetic studies: use of the electronic medical record to enable a genome-wide association study of peripheral arterial disease , 2010, J. Am. Medical Informatics Assoc..

[9]  G. Hripcsak,et al.  Extracting Findings from Narrative Reports: Software Transferability and Sources of Physician Disagreement , 1998, Methods of Information in Medicine.

[10]  Karl Pearson F.R.S..  LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[11]  D. Heitjan,et al.  Distinguishing “Missing at Random” and “Missing Completely at Random” , 1996 .

[12]  S W Tu,et al.  Temporal-abstraction mechanisms in management of clinical protocols. , 1991, Proceedings. Symposium on Computer Applications in Medical Care.

[13]  Alexander A. Morgan,et al.  Validating pathophysiological models of aging using clinical electronic medical records , 2010, J. Biomed. Informatics.

[14]  S. Brunak,et al.  Mining electronic health records: towards better research applications and clinical care , 2012, Nature Reviews Genetics.

[15]  R. Altman,et al.  Data-Driven Prediction of Drug Effects and Interactions , 2012, Science Translational Medicine.

[16]  Homer R. Warner.  Knowledge Sectors for Logical Processing of Patient Data in the Help System , 1978 .

[17]  J. Austin,et al.  Use of natural language processing to translate clinical information from a database of 889,921 chest radiographic reports. , 2002, Radiology.

[18]  Lucila Ohno-Machado,et al.  Natural language processing: an introduction , 2011, J. Am. Medical Informatics Assoc..

[19]  Peter J. F. Lucas,et al.  Dynamic Bayesian networks as prognostic models for clinical patient management , 2008, J. Biomed. Informatics.

[20]  B. Weare,et al.  Empirical Orthogonal Analysis of Pacific Sea Surface Temperatures , 1976 .

[21]  Peter J. Haug,et al.  Exploiting missing clinical data in Bayesian network modeling for predicting medical problems , 2008, J. Biomed. Informatics.

[22]  G. Hripcsak,et al.  A statistical dynamics approach to the study of human health data: resolving population scale diurnal variation in laboratory data. , 2010, Physics letters. A.

[23]  Michael M. Wagner,et al.  Review: Accuracy of Data in Computer-based Patient Records , 1997, J. Am. Medical Informatics Assoc..

[24]  Bud Mishra,et al.  The Temporal Logic of Causal Structures , 2009, UAI.

[25]  George Hripcsak,et al.  Inter-patient distance metrics using SNOMED CT defining relationships , 2006, J. Biomed. Informatics.

[26]  C. Granger.  Investigating causal relations by econometric models and cross-spectral methods , 1969 .

[27]  C. Chute,et al.  Electronic Medical Records for Genetic Research: Results of the eMERGE Consortium , 2011, Science Translational Medicine.

[28]  Christopher G Chute,et al.  An OWL meta-ontology for representing the Clinical Element Model. , 2011, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[29]  D. Blumenthal,et al.  The "meaningful use" regulation for electronic health records. , 2010, The New England journal of medicine.

[30]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[31]  Yuval Shahar,et al.  Medical Temporal-Knowledge Discovery via Temporal Abstraction , 2009, AMIA.

[32]  R. Altman,et al.  Detecting Drug Interactions From Adverse‐Event Reports: Interaction Between Paroxetine and Pravastatin Increases Blood Glucose Levels , 2011, Clinical pharmacology and therapeutics.

[33]  Russ B. Altman,et al.  The utility of general purpose versus specialty clinical databases for research: Warfarin dose estimation from extracted clinical variables , 2010, J. Biomed. Informatics.

[34]  George Hripcsak,et al.  Bias Associated with Mining Electronic Health Records , 2011, Journal of biomedical discovery and collaboration.

[35]  T A Pryor,et al.  Sharing MLM's: an experiment between Columbia-Presbyterian and LDS Hospital. , 1993, Proceedings. Symposium on Computer Applications in Medical Care.

[36]  Philip Scott,et al.  Semantic mapping to simplify deployment of HL7 v3 Clinical Document Architecture , 2012, J. Biomed. Informatics.

[37]  Frank D. Wood,et al.  Hierarchically Supervised Latent Dirichlet Allocation , 2011, NIPS.

[38]  Carlo Combi,et al.  Data mining with Temporal Abstractions: learning rules from time series , 2007, Data Mining and Knowledge Discovery.

[39]  Melissa A. Basford,et al.  Identification of Genomic Predictors of Atrioventricular Conduction: Using Electronic Medical Records as a Tool for Genome Science , 2010, Circulation.

[40]  Wendy A. Wolf,et al.  The eMERGE Network: A consortium of biorepositories linked to electronic medical records data for conducting genomic studies , 2011, BMC Medical Genomics.

[41]  Lawrence M. Fagan,et al.  Extensions to the Time-Oriented Database Model to Support Temporal Reasoning in Medical Expert Systems , 1991, Methods of Information in Medicine.

[42]  Rajesh Gulati,et al.  Selecting a change and evaluating its impact on the performance of a complex adaptive health care delivery system , 2010, Clinical interventions in aging.

[43]  M. Sordo,et al.  Rapid Identification of Myocardial Infarction Risk Associated With Diabetes Medications Using Electronic Medical Records , 2009, Diabetes Care.

[44]  George Hripcsak,et al.  Exploiting time in electronic health record correlations , 2011, J. Am. Medical Informatics Assoc..

[45]  Chunhua Weng,et al.  Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research , 2013, J. Am. Medical Informatics Assoc..

[46]  Christopher G Chute,et al.  Analyzing the heterogeneity and complexity of Electronic Health Record oriented phenotyping algorithms. , 2011, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[47]  M C Romano,et al.  Reconstruction of a system's dynamics from short trajectories. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[48]  George Hripcsak,et al.  Using discordance to improve classification in narrative clinical databases: An application to community-acquired pneumonia , 2007, Comput. Biol. Medicine.