{"id":"W4361279828","doi":"10.1097/hep.0000000000000364","title":"Machine learning algorithm improves the detection of NASH (NAS-based) and at-risk NASH: A development and validation study","year":2023,"lang":"en","type":"article","venue":"Hepatology","topic":"Liver Disease Diagnosis and Treatment","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Vlaamse regering; Amgen; Siemens Healthineers; Newcastle University; Novo Nordisk Fonden; Sanofi; Alberta Innovates Bio Solutions; Astellas Pharma; Eisai; Ipsen; Fonds Wetenschappelijk Onderzoek; Novo Nordisk; Dr. Falk Pharma; National Institute for Health and Care Research; Boston Pharmaceuticals; Pfizer; Kowa Company; Inventiva Pharma; European Commission; European Federation of Pharmaceutical Industries and Associations; Medpace; Intercept Pharmaceuticals; Drugs for Neglected Diseases initiative; Gilead Sciences; AstraZeneca","keywords":"Steatohepatitis; Boosting (machine learning); Nash equilibrium; Gradient boosting; Artificial intelligence; Medicine; Steatosis; Computer science; Machine learning; Algorithm; Fatty liver; Mathematics; Internal medicine; Mathematical optimization; Disease","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.01645257,0.001368559,0.0008551218,0.001062427,0.0003059877,0.0006517299,0.001091546,0.001236296,0.0009492036],"category_scores_gemma":[0.01817048,0.0003769631,0.001264653,0.0006171127,0.0005343016,0.0008175576,0.0008925663,0.001154682,0.0004706422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008180115,"about_ca_system_score_gemma":0.0009256596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003276645,"about_ca_topic_score_gemma":0.002088421,"domain_scores_codex":[0.9944634,0.003890055,0.0002628538,0.0005151886,0.0006501991,0.000218301],"domain_scores_gemma":[0.9814104,0.01292558,0.0006901047,0.001425634,0.003153624,0.0003945597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0105895,0.009487247,0.3168691,0.0005342106,0.002418276,0.0002818369,0.0003325977,0.2345903,0.007158182,0.001027558,0.00551182,0.4111994],"study_design_scores_gemma":[0.0006783533,0.008957625,0.06107514,0.00007431537,0.0004864506,0.0001923427,0.00006665813,0.9196306,0.007214079,0.0004090309,0.001168757,0.0000465751],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9746785,0.001476647,0.02073299,0.0002307659,0.00008230219,0.0003937241,0.0004104974,0.0004082953,0.001586326],"genre_scores_gemma":[0.9738712,0.0003089064,0.0234886,0.00007508916,0.00002776708,0.0001543596,0.001079823,0.00004094248,0.0009534238],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01645257,"threshold_uncertainty_score":0.0870105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01649661596432501,"score_gpt":0.264050420348138,"score_spread":0.247553804383813,"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."}}