ORIGINAL RESEARCH article

Front. Pharmacol.

Sec. Pharmacoepidemiology

Volume 16 - 2025 | doi: 10.3389/fphar.2025.1554650

Adverse drug reaction signal detection via the Long Short-Term Memory model

Provisionally accepted
  • 1Department of Biostatistics, School of Public Health, Health Science Centre, Peking University, Beijing, China
  • 2Department of Genetics, Peking University Cancer Hospital & Institute, Beijing, China
  • 3Center for ADR Monitoring of Guangdong, Guangzhou, China
  • 4National Institute of Health Data Science, Peking University, Beijing, China

The final, formatted version of the article will be published soon.

Drug safety has increasingly become a serious public health problem that threatens health and damages social economy. The common detection methods have the problem of high false positive rate. This study aims to introduce deep learning models into the adverse drug reaction (ADR) signal detection and compare different methods.The data are based on adverse events collected by Center for ADR Monitoring of Guangdong. Traditional statistical methods were used for data preliminary screening.We transformed data into free text, extracted text information and made classification prediction by using the Long Short-Term Memory (LSTM) model. We compared it with the existing signal detection methods, including Logistic Regression, Random Forest, K-NearestNeighbor, and Multilayer Perceptron. The feature importance of the included variables was analyzed.A total of 2,376 ADR signals were identified between January 2018 and December 2019, comprising 448 positive signals and 1,928 negative signals. The sensitivity of the LSTM model based on free text reached 95.16%, and the F1-score was 0.9706. The sensitivity of Logistic Regression model based on feature variables was 86.83%, and the F1-score was 0.9063. The classification results of our model demonstrate superior sensitivity and F1-score compared to traditional methods. Several important variables"Reasons for taking medication"," Serious ADR scenario 4", "Adverse reaction analysis 5", and "Dosage" had an important influence on the result.The application of deep learning models shows potential to improve the detection performance in ADR monitoring.

Keywords: Adverse Drug Reaction, Signal detection, Free text, the Long Short-Term Memory model, Feature importance

Received: 02 Jan 2025; Accepted: 05 Jun 2025.

Copyright: © 2025 Cao, Chi, Yu, Yang, Meng and Jia. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Jinzhu Jia, Department of Biostatistics, School of Public Health, Health Science Centre, Peking University, Beijing, China

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