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

Document Type

Conference Proceeding

Publication Date

12-10-2020

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.

Identifier

85103820472 (Scopus)

ISBN

[9781728162515]

Publication Title

Proceedings 2020 IEEE International Conference on Big Data Big Data 2020

External Full Text Location

https://doi.org/10.1109/BigData50022.2020.9378336

First Page

1388

Last Page

1393

Grant

UL1TR003017

Fund Ref

National Institutes of Health

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