Abstract 4923: A systematic framework for predicting adverse drug reaction signals using medical data

Abstract

Abstract Prescribing a combination of medications is a common strategy to enhance the effectiveness of disease treatment. While this approach aims to improve therapeutic outcomes, it may lead to unintended adverse drug reactions (ADRs) due to interactions between prescribed drugs and individual factors. Recognizing the importance of ADRs, various machine learning models have been developed. However, these models face limitations, particularly in predicting reactions induced by three or more drugs and in accounting for individual patient factors, such as age and medical history. To address these limitations, we propose a systematic framework that leverages medical data for predicting ADR signals. In the framework, the MIMIC-IV database was systematically preprocessed, and a series of machine learning models were developed that predict the ADR signals. The model predictions were validated using the eICU collaborative research database. The machine learning models process multiple inputs, including information on administered drugs, age, gender, ethnicity, and underlying medical conditions to predict ADR signals. The ADR signals are determined by classifying abnormalities in 20 specific laboratory test values, such as hematocrit, creatinine, and hemoglobin. The machine learning models developed in this study hold promise as a valuable tool for assessing potential risks, such as ADRs, associated with the concurrent use of multiple drugs. Citation Format: Junhyeok Jeon, Eujin Hong, Hyun Uk Kim. A systematic framework for predicting adverse drug reaction signals using medical data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4923.