KG-LIME: predicting individualized risk of adverse drug events for multiple sclerosis disease-modifying therapy

• Published in: Journal of the American Medical Informatics Association : JAMIA 2024-08, Vol.31 (8), p.1693-1703
• Main Authors: Patterson, Jason, Tatonetti, Nicholas
• Format: Article
• Language: English
• Subjects: Adult; Drug-Related Side Effects and Adverse Reactions; Electronic Health Records; Female; Humans; Male; Middle Aged; Multiple Sclerosis - drug therapy; Neural Networks, Computer; Risk Assessment
• ISSN: 1067-5027; 1527-974X; 1527-974X
• DOI: 10.1093/jamia/ocae155

The aim of this project was to create time-aware, individual-level risk score models for adverse drug events related to multiple sclerosis disease-modifying therapy and to provide interpretable explanations for model prediction behavior. We used temporal sequences of observational medical outcomes partnership common data model (OMOP CDM) concepts derived from an electronic health record as model features. Each concept was assigned an embedding representation that was learned from a graph convolution network trained on a knowledge graph (KG) of OMOP concept relationships. Concept embeddings were fed into long short-term memory networks for 1-year adverse event prediction following drug exposure. Finally, we implemented a novel extension of the local interpretable model agnostic explanation (LIME) method, knowledge graph LIME (KG-LIME) to leverage the KG and explain individual predictions of each model. For a set of 4859 patients, we found that our model was effective at predicting 32 out of 56 adverse event types (P