Learning Treatment Regimens from Electronic Medical Records
This work addresses the challenge of deriving treatment regimens for patients, such as those with coronary artery disease, from heterogeneous electronic medical records, though it appears incremental in its approach.
The authors tackled the problem of learning treatment regimens from electronic medical records by proposing a data-driven framework that uses a mixed-variate restricted Boltzmann machine and incorporates medical domain knowledge, with results showing it is promising for assisting physicians in clinical decisions.
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.