Abstract
Adverse drug events (ADEs) are a serious health problem that can be life-threatening. While a lot of work on detecting correlation between a drug and an ADE, limited studies have been conducted on personalized ADE risk prediction. Avoiding the drugs with high likelihood of causing severe ADEs helps physicians to provide safer treatments to patients. 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 claim 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 captures characteristics of claim codes and their relationship. Eempirical evaluation shows that the proposed HTNNR model substantially outperforms the comparison methods.
Original language | English (US) |
---|---|
Title of host publication | Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020 |
Editors | Xintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1388-1393 |
Number of pages | 6 |
ISBN (Electronic) | 9781728162515 |
DOIs | |
State | Published - Dec 10 2020 |
Event | 8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States Duration: Dec 10 2020 → Dec 13 2020 |
Publication series
Name | Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020 |
---|
Conference
Conference | 8th IEEE International Conference on Big Data, Big Data 2020 |
---|---|
Country/Territory | United States |
City | Virtual, Atlanta |
Period | 12/10/20 → 12/13/20 |
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Information Systems
- Information Systems and Management
- Safety, Risk, Reliability and Quality
Keywords
- Adverse Drug Events
- Claim Codes
- Neural Networks
- Personalized Risk Assessment