A survey on adverse drug reaction studies: data, tasks and machine learning methods

Abstract

Motivation Adverse drug reaction (ADR) or drug side effect studies play a crucial role in drug discovery. Recently, with the rapid increase of both clinical and non-clinical data, machine learning methods have emerged as prominent tools to support analyzing and predicting ADRs. Nonetheless, there are still remaining challenges in ADR studies. Results In this paper, we summarized ADR data sources and review ADR studies in three tasks: drug-ADR benchmark data creation, drug–ADR prediction and ADR mechanism analysis. We focused on machine learning methods used in each task and then compare performances of the methods on the drug–ADR prediction task. Finally, we discussed open problems for further ADR studies. Availability Data and code are available at https://github.com/anhnda/ADRPModels.

A Data-Driven Medical Decision Framework for Associating Adverse Drug Events with Drug-Drug Interaction Mechanisms

“…PhV implementations have shown promising results in studying and predicting ADEs before their occurrence, for example, through data extraction, collection, and creation [7][8][9][10]. Other applications have focused on predicting ADEs using computational methods such as text mining [11], machine learning [12], deep learning [13], and network models [14]. Such methods often employ inputs that are clinically based, such as electronic patient records [15], clinical notes [16], disease characteristics [17], or drug features [18]; other nonclinical inputs consist of personal messages [19], social media posts [20], and advice from human experts [21].…”

Advancing drug safety science by integrating molecular knowledge with post‐marketing adverse event reports

“…In this approach, TAE profiles generated by MH EFFECT (a technology encompassing the MASE methodology), were used to predict postmarket label changes by incorporating molecular features into ML. Integration of such domain‐specific knowledge may provide further features into advanced ML models studying AEs by encoding chemo‐ and bio‐descriptors about the physical, chemical, and biological characteristics of the involved components of interest (e.g., drugs) 72 . Analyzing large and complex datasets and the ability to discover novel and hidden but precious knowledge in data are key advantages to using ML techniques and may thus be applied to a wide range of predictive safety settings 15,23,72,73 .…”

“…Integration of such domain‐specific knowledge may provide further features into advanced ML models studying AEs by encoding chemo‐ and bio‐descriptors about the physical, chemical, and biological characteristics of the involved components of interest (e.g., drugs). 72 Analyzing large and complex datasets and the ability to discover novel and hidden but precious knowledge in data are key advantages to using ML techniques and may thus be applied to a wide range of predictive safety settings. 15 , 23 , 72 , 73 Notably, in the context of the coronavirus disease 2019 (COVID‐19) pandemic, the FDA issued a landmark emergency use authorization to an enhanced artificial intelligence (AI)‐powered tool to predict AEs.…”

Adverse Drug Reaction Detection From Social Media Based on Quantum Bi-LSTM With Attention

“…Most ADRs are mild to moderate and can be controlled with adequate supervision and monitoring, but few serious ADRs may result in deterioration, shock and even death [4][5][6]. ADRs have become the fourth cause of death in the United States and in similar countries, after heart disease, diabetes and AIDS [7][8][9]. Margraff and Bertram [9] found that direct patient reporting systems exist in 44 countries and represent 9% of total reports, the rest coming from healthcare professionals.…”