{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002789387,0.0002537884,0.0003499632,0.0001656069,0.0006810797,0.00008806422,0.0001802362,0.0004422206,0.0002907816],"category_scores_gemma":[0.0002862254,0.0002491133,0.00004915433,0.0003105055,0.00005543865,0.0008068323,0.0002969874,0.001790574,0.00001916522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001373232,"about_ca_system_score_gemma":0.000346096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009530029,"about_ca_topic_score_gemma":0.00009969353,"domain_scores_codex":[0.9971346,0.0008905348,0.0008231213,0.0002362074,0.0005226304,0.0003929001],"domain_scores_gemma":[0.9987776,0.0002134895,0.0002610753,0.0001690839,0.0001607928,0.0004179042],"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":[0.0004171008,0.003791065,0.1007559,0.001265334,0.0009221892,0.0001326031,0.2741142,0.023802,0.02872907,0.0002850769,0.00009450205,0.565691],"study_design_scores_gemma":[0.0008585299,0.000107315,0.0003071439,0.00007850183,0.0001040387,0.00006656156,0.004750472,0.9292712,0.06288091,0.00003643172,0.001270493,0.0002684646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9816034,0.00004180014,0.01638133,0.00005345789,0.0003761213,0.0006353424,0.000009383334,0.0001546121,0.0007444822],"genre_scores_gemma":[0.9839696,0.00005768765,0.01482537,0.0007654138,0.000106361,0.00007908497,0.00006233469,0.00002358866,0.0001105339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9054691,"threshold_uncertainty_score":0.9999961,"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."}}