Mining Time-Stamped Electronic Health Records Using Referenced Sequences
Anne Woods (1), Craig Meyer (2), Brian Sauer (3, 4), Beth Cohen (2, and 5) ((1) Northern California Institute for Research, Education (2), University of California, San Francisco (3) University of Utah, Division of, Epidemiology, Salt Lake City

TL;DR
This paper introduces a sequence-based method for creating time-interdependent variables from electronic health records, simplifying analysis of temporal relationships in medical data.
Contribution
It presents a novel technique using referenced sequences to generate clinically informed variables, streamlining the analysis process and enabling automation.
Findings
Sequences effectively capture patient medical history aspects.
Method applied to identify data anomalies and trends.
Created variables reveal temporal dependencies in treatment data.
Abstract
Electronic Health Records (EHRs) are typically stored as time-stamped encounter records. Observing temporal relationship between medical records is an integral part of interpreting the information. Hence, statistical analysis of EHRs requires that clinically informed time-interdependent analysis variables (TIAV) be created. Often, formulation and creation of these variables are iterative and requiring custom codes. We describe a technique of using sequences of time-referenced entities as the building blocks for TIAVs. These sequences represent different aspects of patient's medical history in a contiguous fashion. To illustrate the principles and applications of the method, we provide examples using Veterans Health Administration's research databases. In the first example, sequences representing medication exposure were used to assess patient selection criteria for a treatment…
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Taxonomy
TopicsMachine Learning in Healthcare · Bayesian Modeling and Causal Inference · Time Series Analysis and Forecasting
