{"id":"W3185172885","doi":"10.2196/29226","title":"Predicting Antituberculosis Drug–Induced Liver Injury Using an Interpretable Machine Learning Method: Model Development and Validation Study","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Drug-Induced Hepatotoxicity and Protection","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Sanming Project of Medicine in Shenzhen; Science, Technology and Innovation Commission of Shenzhen Municipality","keywords":"Medicine; Receiver operating characteristic; Pyrazinamide; Ethambutol; Liver injury; Aspartate transaminase; Tuberculosis; Internal medicine; Machine learning; Artificial intelligence; Mycobacterium tuberculosis; Pathology","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":[0.007831994,0.001516489,0.000946354,0.001100097,0.0003797478,0.0009278507,0.001170265,0.001094086,0.001026441],"category_scores_gemma":[0.01001518,0.0002872082,0.001296688,0.0005911342,0.000386558,0.0004985278,0.0006481814,0.001347121,0.0002562597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001102476,"about_ca_system_score_gemma":0.001550535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01323051,"about_ca_topic_score_gemma":0.005598918,"domain_scores_codex":[0.9984885,0.0009249346,0.0001097242,0.0002009799,0.0001715861,0.0001041469],"domain_scores_gemma":[0.9914343,0.006210014,0.000451831,0.0004092052,0.001370474,0.0001240897],"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":[0.0008160279,0.001281395,0.05125563,0.0002188551,0.0005677579,0.000183987,0.0001201133,0.885932,0.001644099,0.0003983062,0.0009867345,0.05659497],"study_design_scores_gemma":[0.00002673654,0.0002439199,0.002813369,0.0000160007,0.00003950831,0.00001942701,0.00001149146,0.9961303,0.0004962143,0.0001115576,0.00008418173,0.000007299644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9294571,0.001023166,0.06659338,0.0003219768,0.00007583185,0.0003723439,0.0005066496,0.0004641235,0.00118541],"genre_scores_gemma":[0.9765228,0.0002477966,0.02163332,0.00007204746,0.00002160921,0.0002211878,0.0008536097,0.00001760135,0.0004099503],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01323051,"threshold_uncertainty_score":0.04142004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1489582230874151,"score_gpt":0.4539517250489671,"score_spread":0.3049935019615521,"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."}}