An integrated LSTM-HeteroRGNN model for interpretable opioid overdose risk prediction.

ABSTRACT: Opioid overdose (OD) has become a leading cause of accidental death in the United States, and overdose deaths reached a record high during the COVID-19 pandemic. Combating the opioid crisis requires targeting high-need populations by identifying individuals at risk of OD. While deep learning emerges as a powerful method for building predictive models using large scale electronic health records (EHR), it is challenged by the complex intrinsic relationships among EHR data. Further, its utility is limited by the lack of clinically meaningful explainability, which is necessary for making informed clinical or policy decisions using such models. In this paper, we present LIGHTED, an integrated deep learning model combining long short term memory (LSTM) and graph neural networks (GNN) to predict

SUBMITTER: Dong X 

PROVIDER: S-EPMC9630306 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature