{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002023469,0.00009309284,0.0001844606,0.00008405109,0.0001746924,0.000006439337,0.00001964449,0.00003991004,0.00001019558],"category_scores_gemma":[0.0000537814,0.00006193159,0.00002377217,0.000118191,0.00004992605,0.00001886068,0.00005807917,0.00007729498,0.00000920522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003956741,"about_ca_system_score_gemma":0.00003889931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002889753,"about_ca_topic_score_gemma":0.000177442,"domain_scores_codex":[0.9992966,0.0001169063,0.00016092,0.0001959041,0.0001120016,0.0001176359],"domain_scores_gemma":[0.9995582,0.0001659328,0.00008605346,0.0000992495,0.00003836278,0.00005225107],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006875272,0.0001855591,0.8755446,0.00003442537,0.0001480284,0.00003171976,0.0008719031,0.000001465494,0.0004131701,0.000004554649,0.00000821194,0.1226876],"study_design_scores_gemma":[0.002286515,0.0008313677,0.9631482,0.00001726487,0.0003199302,0.00002410593,0.0005012105,0.01390809,0.01766885,0.00002234468,0.001197501,0.00007458937],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983374,0.0007997122,0.00007736705,0.0002078949,0.00005403328,0.0004542791,0.000007555072,0.00004422705,0.00001748435],"genre_scores_gemma":[0.9992577,0.0002899098,0.0001652168,0.000041765,0.0000136592,0.0001163172,0.00004231879,0.000009895185,0.00006317524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.122613,"threshold_uncertainty_score":0.2525497,"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."}}