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Image Search Results
Journal: Scientific Data
Article Title: An Evaluation Benchmark for Adverse Drug Event Prediction from Clinical Trial Results
doi: 10.1038/s41597-025-04718-1
Figure Lengend Snippet: Generative model pipeline used for ADE prediction. A prompt is created based on the input features, and OpenBioLLM-8B is tasked to generate a list of ADEs.
Article Snippet: To assess the impact of model scaling and domain-specific pre-training on ADE prediction, we additionally fine-tuned a range of
Techniques:
Journal: Scientific Data
Article Title: An Evaluation Benchmark for Adverse Drug Event Prediction from Clinical Trial Results
doi: 10.1038/s41597-025-04718-1
Figure Lengend Snippet: Performance comparison of discriminative (ChemBERTa-77M-MLM & PubMedBERT) and generative (OpenBioLLM-8B) models on the CT-ADE-SOC test split using different feature sets (S, SG, SGE). The AUROC metric cannot be computed for the generative model as it does not produce raw probabilities. All metrics are micro-averaged. Tabular values are available in Supplementary Table .
Article Snippet: To assess the impact of model scaling and domain-specific pre-training on ADE prediction, we additionally fine-tuned a range of
Techniques: Comparison
28 test split. All metrics are micro-averaged." width="100%" height="100%">
Journal: Scientific Data
Article Title: An Evaluation Benchmark for Adverse Drug Event Prediction from Clinical Trial Results
doi: 10.1038/s41597-025-04718-1
Figure Lengend Snippet: Performance metrics of various models using the SGE feature set evaluated on the CT-ADE-SOC
Article Snippet: To assess the impact of model scaling and domain-specific pre-training on ADE prediction, we additionally fine-tuned a range of
Techniques: