Modeling drug exposure data in electronic medical records: an application to warfarin.

Identification of patients' drug exposure information is critical to drug-related research that is based on electronic medical records (EMRs). Drug information is often embedded in clinical narratives and drug regimens change frequently because of various reasons like intolerance or insurance issues, making accurate modeling challenging. Here, we developed an informatics framework to determine patient drug exposure histories from EMRs by combining natural language processing (NLP) and machine learning (ML) technologies. Our framework consists of three phases: 1) drug entity recognition - identifying drug mentions; 2) drug event detection - labeling drug mentions with a status (e.g., "on" or "stop"); and 3) drug exposure modeling - predicting if a patient is taking a drug at a given time using the status and temporal information associated with the mentions. We applied the framework to determine patient warfarin exposure at hospital admissions and achieved 87% precision, 79% recall, and an area under the receiver-operator characteristic curve of 0.93.

[1]  Christopher G. Chute,et al.  Maximum entropy modeling for mining patient medication status from free text , 2002, AMIA.

[2]  Konstantinos Kalpakis,et al.  Distance measures for effective clustering of ARIMA time-series , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[3]  Manish Sarkar,et al.  Characterization of medical time series using fuzzy similarity-based fractal dimensions , 2003, Artif. Intell. Medicine.

[4]  M. Pearson,et al.  Medication Reconciliation: A Necessity in Promoting a Safe Hospital Discharge , 2006, Journal for healthcare quality : official publication of the National Association for Healthcare Quality.

[5]  Sunghwan Sohn,et al.  Classification of medication status change in clinical narratives. , 2010, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[6]  George Hripcsak,et al.  A temporal constraint structure for extracting temporal information from clinical narrative , 2006, J. Biomed. Informatics.

[7]  Riccardo Bellazzi,et al.  Temporal data mining for the quality assessment of hemodialysis services , 2005, Artif. Intell. Medicine.

[8]  Michael E Matheny,et al.  Prevalence and Clinical Significance of Discrepancies within Three Computerized Pre-Admission Medication Lists. , 2010, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[9]  Russell A Wilke,et al.  Relative impact of CYP3A genotype and concomitant medication on the severity of atorvastatin-induced muscle damage , 2005, Pharmacogenetics and genomics.

[10]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

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

[12]  T. Warren Liao,et al.  Clustering of time series data - a survey , 2005, Pattern Recognit..

[13]  Suzanne L. West,et al.  Information Extraction from Medical Notes , 2007 .

[14]  Son Doan,et al.  Integrating existing natural language processing tools for medication extraction from discharge summaries , 2010, J. Am. Medical Informatics Assoc..

[15]  P. Pronovost,et al.  Medication reconciliation: a practical tool to reduce the risk of medication errors. , 2003, Journal of critical care.

[16]  Jianhua Li,et al.  Medication Reconciliation Using Natural Language Processing and Controlled Terminologies , 2007, MedInfo.

[17]  Alexander Turchin,et al.  Identification of Inactive Medications in Narrative Medical Text , 2008, AMIA.

[18]  Tejal K. Gandhi,et al.  Design and implementation of an application and associated services to support interdisciplinary medication reconciliation efforts at an integrated healthcare delivery network. , 2006, Journal of the American Medical Informatics Association : JAMIA.

[19]  Hua Xu,et al.  Extracting timing and status descriptors for colonoscopy testing from electronic medical records , 2010, J. Am. Medical Informatics Assoc..

[20]  David L. Reich,et al.  Extraction and Mapping of Drug Names from Free Text to a Standardized Nomenclature , 2007, AMIA.

[21]  George Hripcsak,et al.  Research Paper: The Evaluation of a Temporal Reasoning System in Processing Clinical Discharge Summaries , 2008, J. Am. Medical Informatics Assoc..

[22]  Russell A Wilke,et al.  Biobanking and pharmacogenomics. , 2010, Pharmacogenomics.

[23]  Son Doan,et al.  Application of information technology: MedEx: a medication information extraction system for clinical narratives , 2010, J. Am. Medical Informatics Assoc..

[24]  Sotirios Chatzis,et al.  Robust Sequential Data Modeling Using an Outlier Tolerant Hidden Markov Model , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.