{"id":"W4394962126","doi":"10.1001/jamahealthforum.2024.0625","title":"A Novel Machine Learning Algorithm for Creating Risk-Adjusted Payment Formulas","year":2024,"lang":"en","type":"article","venue":"JAMA Health Forum","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Medical diagnosis; Health care; Diagnosis code; Medicine; Computer science; Vagueness; Machine learning; Payment; Artificial intelligence; Actuarial science; Fuzzy logic; Radiology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001118073,0.0001730439,0.000312693,0.0001859489,0.0005807641,0.00007985135,0.00006177548,0.0001471322,0.00007859457],"category_scores_gemma":[0.0005530927,0.0001470334,0.0001398742,0.0003138942,0.00002555633,0.0001596304,0.00002812187,0.0006189449,0.00007099652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004926658,"about_ca_system_score_gemma":0.0007576994,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008692755,"about_ca_topic_score_gemma":0.0005772472,"domain_scores_codex":[0.9979257,0.00004993225,0.0006856036,0.0003762417,0.0002489478,0.0007135862],"domain_scores_gemma":[0.9986122,0.0005162158,0.0001746311,0.0001900947,0.0001723953,0.0003344776],"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":[0.00006932136,0.0001067521,0.004046425,0.0008125962,0.00003665044,0.000002821453,0.002774502,0.00002979912,0.00006476422,0.000888961,0.004564648,0.9866028],"study_design_scores_gemma":[0.0002884002,0.001870557,0.001219909,0.0009804171,0.00006533684,0.00008325424,0.005519751,0.7335434,0.001350982,0.001148928,0.2537329,0.0001961016],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04112337,0.01425228,0.7942779,0.1395546,0.003783676,0.004436407,0.0003364999,0.0008680897,0.001367186],"genre_scores_gemma":[0.8617446,0.002317657,0.1139109,0.01067764,0.00326342,0.0007322064,0.001025977,0.0001684633,0.006159203],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9864067,"threshold_uncertainty_score":0.9979085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1123051258688917,"score_gpt":0.4215022981014511,"score_spread":0.3091971722325594,"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."}}