A Treatment Engine by Predicting Next-Period Prescriptions.

Recent years have witnessed an opportunity for improving healthcare efficiency and quality by mining Electronic Medical Records (EMRs). This paper is aimed at developing a treatment engine, which learns from historical EMR data and provides a patient with next-period prescriptions based on disease conditions, laboratory results, and treatment records of the patient. Importantly, the engine takes consideration of both treatment records and physical examination sequences which are not only heterogeneous and temporal in nature but also often with different record frequencies and lengths. Moreover, the engine also combines static information (e.g., demographics) with the temporal sequences to provide personalized treatment prescriptions to patients. In this regard, a novel Long Short-Term Memory (LSTM) learning framework is proposed to model inter-correlations of different types of medical sequences by connections between hidden neurons. With this framework, we develop three multifaceted LSTM models: Fully Connected Heterogeneous LSTM, Partially Connected Heterogeneous LSTM, and Decomposed Heterogeneous LSTM. The experiments are conducted on two datasets: one is the public MIMIC-III ICU data, and the other comes from several Chinese hospitals. Experimental results reveal the effectiveness of the framework and the three models. The work is deemed important and meaningful for both academia and practitioners in the realm of medical treatment and prediction, as well as in other fields of applications where intelligent decision support becomes pervasive.