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 effec...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 approach we proposed can effectively mine typical treatment duration patterns from EMRs and recommend the appropriate treatment regimens for patients based on their admission information.展开更多
基金The authors would like to thank the anonymous referees for their help to improve the quality of the paper. This research was supported in part by the National Natural Science Foundation of China under Grant Nos. 71771034 and 71421001Science and Technology Program of Jieyang under Grant No. 2017xm041+1 种基金China Postdoctoral Science Foundation under Grant No. 2017M620054, and the Scientific and Technological Innovation Foundation of Dalian under Grant No. 2018J11CY009This paper is a significantly extended and revised version of the conference paper presented at KSS-2018.
文摘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 approach we proposed can effectively mine typical treatment duration patterns from EMRs and recommend the appropriate treatment regimens for patients based on their admission information.
