{"id":"W3199673018","doi":"10.2196/32730","title":"Adverse Drug Event Prediction Using Noisy Literature-Derived Knowledge Graphs: Algorithm Development and Validation","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Pharmacovigilance and Adverse Drug Reactions","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Machine learning; Artificial intelligence; Computer science; Inference; Natural language processing; Representation (politics); Pharmacovigilance; Named-entity recognition; Benchmark (surveying); Graph; Theoretical computer science; Task (project management); Medicine; Adverse effect","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":[0.006898103,0.00175339,0.001641327,0.00389572,0.0008283948,0.002096794,0.003576012,0.003121845,0.002819025],"category_scores_gemma":[0.03706481,0.0006440707,0.001742624,0.002641519,0.001053376,0.00232724,0.002201485,0.002980775,0.001343592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002195811,"about_ca_system_score_gemma":0.003349967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01045712,"about_ca_topic_score_gemma":0.008073107,"domain_scores_codex":[0.9969895,0.001206436,0.0002827964,0.0008385442,0.0005204944,0.0001623559],"domain_scores_gemma":[0.9676416,0.02647048,0.001252669,0.001822202,0.002488755,0.0003242635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005312287,0.0006555608,0.008732663,0.0004833593,0.0003425139,0.0002476037,0.0001300198,0.7009709,0.0008334824,0.003199463,0.007362144,0.276511],"study_design_scores_gemma":[0.00005269743,0.00004485589,0.0004117191,0.00003300344,0.0000253955,0.0000459455,0.00002076907,0.9945481,0.0003243127,0.003977094,0.0005094769,0.0000065711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1494045,0.006364369,0.8208795,0.002464835,0.0003456219,0.001352684,0.005732646,0.00984826,0.003607721],"genre_scores_gemma":[0.4347471,0.001600234,0.5486302,0.0006214929,0.0001883206,0.00120562,0.01138836,0.000219164,0.001399491],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01045712,"threshold_uncertainty_score":0.03648108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05769689761978077,"score_gpt":0.4143184461319592,"score_spread":0.3566215485121785,"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."}}