Learning Treatment Regimens from Electronic Medical Records

TL;DR

This paper introduces a flexible, data-driven framework that leverages heterogeneous electronic medical records and domain knowledge to learn personalized treatment regimens, demonstrated through coronary artery disease case studies.

Contribution

It proposes a novel framework combining mixed-variate restricted Boltzmann machines and domain knowledge to derive treatment regimens from complex EMR data.

Findings

Framework effectively utilizes diverse patient data.

Capable of assisting clinical decision-making.

Shows promising results in coronary artery disease case study.

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.