{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006039744,0.0002438251,0.0002596735,0.0001652934,0.0004041421,0.00004067874,0.0001420231,0.000356286,0.0004685351],"category_scores_gemma":[0.00007212684,0.0002314716,0.00009045586,0.0004605959,0.0001501407,0.0006167429,0.0001470949,0.0009833598,0.00009891827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001391963,"about_ca_system_score_gemma":0.0005419534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001377526,"about_ca_topic_score_gemma":0.000002841214,"domain_scores_codex":[0.9981205,0.0001866242,0.0007354376,0.0001830117,0.0003888888,0.0003855162],"domain_scores_gemma":[0.9987504,0.0001596338,0.0001982084,0.0001477726,0.0002216421,0.0005223688],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003446825,0.004815149,0.008187792,0.003083912,0.002226425,0.0008475531,0.2159423,0.002376976,0.03098201,0.003988313,0.04018462,0.6870202],"study_design_scores_gemma":[0.00535288,0.00004291536,0.001367907,0.0006381242,0.0003037597,0.0006270825,0.007317229,0.3303757,0.1105726,0.0004207882,0.5422592,0.0007218607],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9899678,0.0009470413,0.00411362,0.0002544098,0.001898213,0.0004421627,0.0001005379,0.0001807298,0.002095545],"genre_scores_gemma":[0.9711158,0.00363014,0.01664483,0.004793929,0.0008886034,0.0002563463,0.001374211,0.00005555812,0.001240566],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6862984,"threshold_uncertainty_score":0.9439139,"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."}}