{"id":"W3157054249","doi":"","title":"A BERT-based Model for Drug-Drug Interaction Extraction from Drug Labels.","year":2019,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Drug; Computer science; Extraction (chemistry); Drug-drug interaction; Pharmacology; Chromatography; Chemistry; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00114955,0.001016644,0.001015948,0.003828435,0.0007318042,0.001372243,0.001995637,0.001440329,0.004308956],"category_scores_gemma":[0.004650541,0.0004200896,0.001983872,0.003386113,0.0004428853,0.002010185,0.00141536,0.001163547,0.002484508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001624032,"about_ca_system_score_gemma":0.002660746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02541445,"about_ca_topic_score_gemma":0.03872655,"domain_scores_codex":[0.9992068,0.0001598282,0.00009651579,0.0001800537,0.0002895431,0.00006721017],"domain_scores_gemma":[0.9983221,0.000986367,0.00009523365,0.0001306555,0.0003862086,0.00007948347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001356213,0.0005589052,0.0112965,0.001744455,0.000659356,0.001175851,0.0003893266,0.2742526,0.006842711,0.05459466,0.06232321,0.5848062],"study_design_scores_gemma":[0.0000457391,0.00008208342,0.001540494,0.0001073433,0.0001508635,0.0002601949,0.00006350008,0.9250451,0.001809081,0.05006654,0.02080156,0.0000274756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02056055,0.003704746,0.9336427,0.002716886,0.0002930825,0.0007480424,0.02062582,0.00975427,0.007953835],"genre_scores_gemma":[0.3264592,0.002384609,0.6227697,0.00123204,0.0002003234,0.001477276,0.03538312,0.0004463292,0.009647438],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02541445,"threshold_uncertainty_score":0.05053306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009371832549338802,"score_gpt":0.2796768935848783,"score_spread":0.2703050610355395,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}