Prediction of adverse drug reactions using demographic and non-clinical drug characteristics in FAERS data.

ABSTRACT: The presence of adverse drug reactions (ADRs) is an ongoing public health concern. While traditional methods to discover ADRs are very costly and limited, it is prudent to predict ADRs through non-invasive methods such as machine learning based on existing data. Although various studies exist regarding ADR prediction using non-clinical data, a process that leverages both demographic and non-clinical data for ADR prediction is missing. In addition, the importance of individual features in ADR prediction has yet to be fully explored. This study aims to develop an ADR prediction model based on demographic and non-clinical data, where we identify the highest contributing factors. We focus our efforts on 30 common and severe ADRs reported to the Food and Drug Administration (FDA) between 2012 a

SUBMITTER: Farnoush A 

PROVIDER: S-EPMC11464664 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature