Mining Drug-Drug Interaction Induced Adverse Effects from Health Record Databases

Recent advances in large-scale electronic health record database techniques provide exciting new opportunities to the study of drug safety. Drug-drug interactions (DDIs), a major cause of adverse drug events (ADEs), are a serious global health concern, and a severe detriment to public health. The scale of DDIs involving three or more drugs (also called high-order DDIs) has posed a prohibitory challenge for its molecular pharmacology and clinical research, which motivates alternative strategies such as mining health record data. This project aims to develop large-scale computational strategies and effective software tools for mining high-order DDI effects from health record databases, in order to yield novel discoveries in drug safety, and ultimately to benefit national health and well being.

To achieve the above goal, this project is designed to complete four specific tasks. Task 1 aims to develop a novel statistical framework to discover high-order DDI signals associated with ADEs from health record databases. Task 2 aims to study a novel drug safety problem for mining directional DDI signals. Task 3 aims to develop an innovative approach for mining directional DDI patterns at the drug-group level. Task 4 is devoted to software development, evaluation and validation. The project applies these methods to analyze three independent databases, packages method implementations into a user-friendly software toolkit, and releases the toolkit to the public. This project not only facilitates the development of novel computational techniques in drug safety research, but also addresses emerging scientific questions in modeling, mining, and visual exploration of complex data such as the health record data. The project's educational activities include course development, student mentoring and advising, and involvement of minority and underrepresented students in research activities.

Research Products

  • High-Order DDI Mining

    Danai Chasioti, Xiaohui Yao, Pengyue Zhang, Sara Quinney, Xia Ning*, Lang Li, and Li Shen. Mining and visualizing the network of directional drug interaction effects. International School and Confernece on Network Science (NetSci), 2017. [ bib ]

    Xia Ning*, Lang Li, and Li Shen. Pattern discovery from directional high-order drug-drug interaction relations. International School and Confernece on Network Science (NetSci), 2017. [ bib ]

    D Chasioti, X Yao, Pengyue Zhang, Xia Ning*, Lang Li, and Li Shen. Mining directional drug interaction effects on myopathy using the faers database. In Pac Symp Biocomput, PSB, Big Island of Hawaii, January 3-7,2017. [ bib ]

This material is based upon work supported by the National Science Foundation under Grant Number IIS-1622526. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.