Mining Typical Treatment Duration Patterns for Rational Drug Use from Electronic Medical Records

Abstract

Rational drug use requires that patients receive medications for an adequate period of time. The adequate duration time of medications not only improve the therapeutic effect of medicines, but also reduce the side effects and adverse reactions of medicines. This paper proposes a data-driven method to mine typical treatment duration patterns for rational drug use from electronic medical records (EMRs). Firstly, a quintuple is defined to describe drug use duration statistics (DUDS) for each drug and treatment record is further represented with DUDS vector (DUDSV). Next a similarity measure method is adopted to compute the similarity between treatment records. Meanwhile, a clustering algorithm is used to cluster all patient treatment records to extract typical treatment duration patterns including typical drug sets, effective drug use day sets, and the DUDSs of each typical drug. Then the extracted typical treatment duration patterns are evaluated and annotated based on patients’ demographic information, disease severity scores, treatment outcome and diagnostic information. Finally, a real-world EMR dataset is performed to indicate that the approachwe proposed can effectively mine typical treatment duration patterns from EMRs and recommend the appropriate treatment regimens for patients based on their admission information.

Keywords

EMR data mining / rational drug use / typical treatment duration pattern / similarity measure / clustering

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