JMIR Med Inform. 2025 Jul 10;13:e67513. doi: 10.2196/67513.
ABSTRACT
BACKGROUND: Adverse drug reactions (ADRs) pose serious risks to patient health, and effectively predicting and managing them is an important public health challenge. Given the complexity and specificity of biomedical text data, the traditional context-independent word embedding model, Word2Vec, has limitations in fully reflecting the domain specificity of such data. Although Bidirectional Encoder Representations from Transformers (BERT)-based models pretrained on biomedical corpora have demonstrated high performance in ADR-related studies, research using these models to predict previously unknown drug-side effect relationships remains insufficient.
OBJECTIVE: This study proposes a method for predicting drug-side effect relationships by leveraging the parametric knowledge embedded in biomedical BERT models. Through this approach, we predict promising candidates for potential drug-side effect relationships with unknown causal mechanisms by leveraging parametric knowledge from biomedical BERT models and embedding vector similarities of known relationships.
METHODS: We used 158,096 pairs of drug-side effect relationships from the side effect resource (SIDER) database to generate an adjacency matrix and calculate the cosine similarity between word embedding vectors of drugs and side effects. Relation scores were calculated for 8,235,435 drug-side effect pairs using this similarity. To evaluate the prediction accuracy of drug-side effect relationships, the area under the curve (AUC) value was measured using the calculated relation score and 158,096 known drug-side effect relationships from SIDER.
RESULTS: The clagator/biobert_v1.1 model achieved an AUC of 0.915 at an optimal threshold of 0.289, outperforming the existing Word2Vec model with an AUC of 0.848. The BERT-based models pretrained on the biomedical corpus outperformed the vanilla BERT model with an AUC of 0.857. External validation with the FDA (Food and Drug Administration) Adverse Event Reporting System data, using Fisher exact test based on 8,235,435 predicted drug-side effect pairs and 901,361 known relationships, confirmed high statistical significance (P<.001) with an odds ratio of 4.822. In addition, a literature review of predicted drug-side effect relationships not confirmed in the SIDER database revealed that these relationships have been reported in recent studies published after 2016.
CONCLUSIONS: This study introduces a method for extracting drug-side effect relationships embedded in parameters of language models pretrained on biomedical corpora and using this information to predict previously unknown drug-side effect relationships. We found that BERT-based models pretrained with biomedical corpora consider contextual information and achieve better performance in drug-side effect relationship prediction. External validation using the FDA Adverse Event Reporting System dataset and the literature review of certain cases confirmed high statistical significance, demonstrating practical applicability. These results highlight the utility of natural language processing-based approaches for predicting and managing ADR.
PMID:40638775 | DOI:10.2196/67513
