Learning Treatment Regimens from Electronic Medical Records

2018/06/16 by Khanh-Hung Hoang, Hoang, Khanh-Hung, Tu-Bao Ho +1
Computer Science · Mathematics · #Artificial Intelligence (cs.AI) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Healthcare #Statistical Methods and Inference #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1806.07461

openalex publication_date 2018/06/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Appropriate treatment regimens play a vital role in improving patient health status. Although some achievements have been made, few of the recent studies of learning treatment regimens have exploited different kinds of patient information due to the difficulty in adopting heterogeneous data to many data mining methods. Moreover, current studies seem too rigid with fixed intervals of treatment periods corresponding to the varying lengths of hospital stay. To this end, this work proposes a generic data-driven framework which can derive group-treatment regimens from electronic medical records by utilizing a mixed-variate restricted Boltzmann machine and incorporating medical domain knowledge. We conducted experiments on coronary artery disease as a case study. The obtained results show that the framework is promising and capable of assisting physicians in making clinical decisions.

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