Yiqing Zhao: Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 750 N Lake Shore Drive, Room 11-189, Chicago, IL, 60611, USA.
Yue Yu: Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, 55902, USA.
Hanyin Wang: Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 750 N Lake Shore Drive, Room 11-189, Chicago, IL, 60611, USA.
Yikuan Li: Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 750 N Lake Shore Drive, Room 11-189, Chicago, IL, 60611, USA.
Yu Deng: Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 750 N Lake Shore Drive, Room 11-189, Chicago, IL, 60611, USA.
Guoqian Jiang: Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, 55902, USA.
Yuan Luo: Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 750 N Lake Shore Drive, Room 11-189, Chicago, IL, 60611, USA. yuan.luo@northwestern.edu. ORCID
Monitoring adverse drug events or pharmacovigilance has been promoted by the World Health Organization to assure the safety of medicines through a timely and reliable information exchange regarding drug safety issues. We aim to discuss the application of machine learning methods as well as causal inference paradigms in pharmacovigilance. We first reviewed data sources for pharmacovigilance. Then, we examined traditional causal inference paradigms, their applications in pharmacovigilance, and how machine learning methods and causal inference paradigms were integrated to enhance the performance of traditional causal inference paradigms. Finally, we summarized issues with currently mainstream correlation-based machine learning models and how the machine learning community has tried to address these issues by incorporating causal inference paradigms. Our literature search revealed that most existing data sources and tasks for pharmacovigilance were not designed for causal inference. Additionally, pharmacovigilance was lagging in adopting machine learning-causal inference integrated models. We highlight several currently trending directions or gaps to integrate causal inference with machine learning in pharmacovigilance research. Finally, our literature search revealed that the adoption of causal paradigms can mitigate known issues with machine learning models. We foresee that the pharmacovigilance domain can benefit from the progress in the machine learning field.
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