On the road to explainable AI in drug-drug interactions prediction: A systematic review

Thanh Hoa Vo, Ngan Thi Kim Nguyen, Quang Hien Kha, Nguyen Quoc Khanh Le

研究成果: 雜誌貢獻回顧型文獻同行評審

摘要

Over the past decade, polypharmacy instances have been common in multi-diseases treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected adverse drug events (ADEs) in multiple regimens therapy remain a significant issue. Since artificial intelligence (AI) is ubiquitous today, many AI prediction models have been developed to predict DDIs to support clinicians in pharmacotherapy-related decisions. However, even though DDI prediction models have great potential for assisting physicians in polypharmacy decisions, there are still concerns regarding the reliability of AI models due to their black-box nature. Building AI models with explainable mechanisms can augment their transparency to address the above issue. Explainable AI (XAI) promotes safety and clarity by showing how decisions are made in AI models, especially in critical tasks like DDI predictions. In this review, a comprehensive overview of AI-based DDI prediction, including the publicly available source for AI-DDIs studies, the methods used in data manipulation and feature preprocessing, the XAI mechanisms to promote trust of AI, especially for critical tasks as DDIs prediction, the modeling methods, is provided. Limitations and the future directions of XAI in DDIs are also discussed.
原文英語
頁(從 - 到)2112-2123
頁數12
期刊Computational and Structural Biotechnology Journal
20
DOIs
出版狀態已發佈 - 1月 2022

ASJC Scopus subject areas

  • 生物技術
  • 生物物理學
  • 結構生物學
  • 生物化學
  • 遺傳學
  • 電腦科學應用

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