TY  - JOUR
PY  - 2023//
TI  - An integrated LSTM-HeteroRGNN model for interpretable opioid overdose risk prediction
JO  - Artificial intelligence in medicine
A1  - Dong, Xinyu
A1  - Wong, Rachel
A1  - Lyu, Weimin
A1  - Abell-Hart, Kayley
A1  - Deng, Jianyuan
A1  - Liu, Yinan
A1  - Hajagos, Janos G.
A1  - Rosenthal, Richard N.
A1  - Chen, Chao
A1  - Wang, Fusheng
SP  - e102439
EP  - e102439
VL  - 135
IS  - 
N2  - 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 patients' OD risk. The LIGHTED model can incorporate the temporal effects of disease progression and the knowledge learned from interactions among clinical features. We evaluated the model using Cerner's Health Facts database with over 5 million patients. Our experiments demonstrated that the model outperforms traditional machine learning methods and other deep learning models. We also proposed a novel interpretability method by exploiting embeddings provided by GNNs to cluster patients and EHR features respectively, and conducted qualitative feature cluster analysis for clinical interpretations. Our study shows that LIGHTED can take advantage of longitudinal EHR data and the intrinsic graph structure of EHRs among patients to provide effective and interpretable OD risk predictions that may potentially improve clinical decision support.<p />  <p>Language: en</p>
LA  - en
SN  - 0933-3657
UR  - http://dx.doi.org/10.1016/j.artmed.2022.102439
ID  - ref1
ER  -