Towards Instructing Disease-Drug Link Prediction with Social Determinants of Health
Authors: 
Yashaswi Galhotra, 
Ying Ding, 
Li Shen, Huanmei Wu, Tianlong Chen, Kaixiong ZhouAuthors Info & Claims
Yashaswi Galhotra, Ying Ding, Li Shen, Huanmei Wu, Tianlong Chen, Kaixiong ZhouAuthors Info & ClaimsArticle No.: 41, Pages 1 - 6
Abstract
In this study, we emphasize the importance of incorporating Social Determinants of Health (SDoH)---such as economic, educational, environmental, and behavioral factors---into personalized medicine. These factors are often overlooked by researchers due to challenges like fragmented knowledge, non-standardized research repositories, and evolving ontologies. Excluding SDoH factors limits the accuracy and effectiveness of disease-drug interaction predictions, which are essential for precision medicine. To overcome these challenges, we introduced the BioSocialNet framework, which integrates SDoH factors into knowledge graph construction and uses Graph Convolutional Networks (GCN) for more accurate predictions. BioSocialNet consists of two key components: (1) construction of a comprehensive knowledge graph that merges biomedical and SDoH data, and (2) deployment of GCNs that model the complex interactions between these factors. We applied this framework to incorporate SDoH data from the MIMIC III dataset into PrimeKG, a comprehensive knowledge graph that includes a wide range of diseases, drugs, and biomedical interactions. We then trained a GCN model on this enriched graph, resulting in a significant improvement in disease-drug linkage prediction accuracy, from 88.05% to 94.08%. This finding underscores the pivotal role of SDoH in enhancing the precision and implications of personalized medicine, providing a clear pathway for more accurate and equitable healthcare interventions.
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November 2024
614 pages
ISBN:9798400713026
DOI:10.1145/3698587
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Published: 16 December 2024
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