Decision Support System for Chronic Diseases Based on Drug-Drug Interactions

• URL: http://arxiv.org/abs/2303.02405v1
• Date: Sat, 4 Mar 2023 12:44:38 GMT
• Title: Decision Support System for Chronic Diseases Based on Drug-Drug Interactions
• Authors: Tian Bian, Yuli Jiang, Jia Li, Tingyang Xu, Yu Rong, Yi Su, Timothy Kwok, Helen Meng, Hong Cheng
• Abstract summary: This paper presents a Decision Support System, called DSSDDI, based on drug-drug interactions to support doctors prescribing decisions. The DDI module learns safer and more effective drug representations from the drug-drug interactions. The MD module considers the representations of patients and drugs as context, DDI and patients' similarity as treatment, and medication use as outcome.
• Score: 67.9840225237587
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Many patients with chronic diseases resort to multiple medications to relieve various symptoms, which raises concerns about the safety of multiple medication use, as severe drug-drug antagonism can lead to serious adverse effects or even death. This paper presents a Decision Support System, called DSSDDI, based on drug-drug interactions to support doctors prescribing decisions. DSSDDI contains three modules, Drug-Drug Interaction (DDI) module, Medical Decision (MD) module and Medical Support (MS) module. The DDI module learns safer and more effective drug representations from the drug-drug interactions. To capture the potential causal relationship between DDI and medication use, the MD module considers the representations of patients and drugs as context, DDI and patients' similarity as treatment, and medication use as outcome to construct counterfactual links for the representation learning. Furthermore, the MS module provides drug candidates to doctors with explanations. Experiments on the chronic data collected from the Hong Kong Chronic Disease Study Project and a public diagnostic data MIMIC-III demonstrate that DSSDDI can be a reliable reference for doctors in terms of safety and efficiency of clinical diagnosis, with significant improvements compared to baseline methods.

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