Xiangyu "Sean" Gao

Sean Ph.D. Student in Business Data Science (Advisor: Dr. Yi Chen)
Martin Tuchman School of Management
New Jersey Institute of Technology

Central Avenue Building (CAB) 2018
University Heights
Newark, NJ, 07102-1982

Email: xg77 [at] njit [dot] edu

LinkedIn GitHub Google Scholar

Short Bio

I am currently a third-year Ph.D. student in Business Data Science at the Martin Tuchman School of Management in New Jersey Institute of Technology. My research interests focus on developing data mining and machine learning techniques for healthcare applications.

Prior to this, I worked as a Data Category Manager for Standard Media Index (SMI) for three years.

I received my Master of Science degree in Marketing Intelligence from Fordham University Gabelli School of Business and Bachelor of Science degree in Business Administration from New York Institute of Technology.


October 20, 2020 One paper accepted by IEEE BigData 2020
November 15, 2020 One paper accepted by IEEE BIBM 2020



Patient ADE Risk Prediction through Hierarchical Time-Aware Neural Network Using Claim Codes

Adverse drug events (ADEs) are a serious health problem that can be life-threatening. While a lot of studies have been performed on detect correlation between a drug and an AE, limited studies have been conducted on personalized ADE risk prediction. Among treatment alternatives, avoiding the drug that has high likelihood of causing severe AE can help physicians to provide safer treatment to patients. Existing work on personalized ADE risk prediction uses the information obtained in the current medical visit. However, on the other hand, medical history reveals each patients unique characteristics and comprehensive medical information. The goal of this study is to assess personalized ADE risks that a target drug may induce on a target patient, based on patient medical history recorded in claims codes, which provide information about diagnosis, drugs taken, related medical supplies besides billing information. We developed a HTNNR model (Hierarchical Time-aware Neural Network for ADE Risk) that capture characteristics of claim codes and their relationship. The empirical evaluation show that the proposed HTNNR model substantially outperforms the comparison methods, especially for rare drugs. [arXiv]

Teaching Experience


IEEE Transactions on Knowledge and Data Engineering (TKDE)