Abstract. We will introduce 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. 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.

Biography. Dr. Jin Bo, Dalian University of Technology. His research interest includes data mining, big data analysis, innovation management, and business intelligence. He has hosted several national funding. He has published more than 70 papers in related fields. In recent years, many papers have been published in the top conferences of data mining (KDD, AAAI, ICDM, SDM, PAKDD, etc.). He has served as PC of KDD, AAAI, ICDM, SDM. He is senior members of ACM, IEEE and CCF.