{"id":"W4288058732","doi":"10.2196/37578","title":"Predicting Readmission Charges Billed by Hospitals: Machine Learning Approach","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Heart Failure Treatment and Management","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Machine learning; Medicine; Artificial intelligence","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00073695,0.0001861641,0.0003423306,0.0001342921,0.0003489211,0.00002411667,0.0001542298,0.00009506121,0.002359979],"category_scores_gemma":[0.0001950837,0.0001362964,0.00009784015,0.0002772689,0.0000492278,0.000135012,0.0003220235,0.0007779946,0.00005504467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001282119,"about_ca_system_score_gemma":0.0001043265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002223963,"about_ca_topic_score_gemma":3.606845e-7,"domain_scores_codex":[0.9974999,0.00007169078,0.0005696931,0.000134975,0.001363932,0.0003597464],"domain_scores_gemma":[0.9990517,0.00005841849,0.0001590227,0.0002272681,0.00003110602,0.0004724951],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004834096,0.003282354,0.1149584,0.002202417,0.0008030775,0.0002141645,0.03726541,0.00009560597,0.0002103917,0.0007713658,0.7001424,0.1395711],"study_design_scores_gemma":[0.00335487,0.001089613,0.0004351763,0.0001043371,0.00009526325,0.0001243109,0.007875205,0.1393749,0.00007104793,0.00001108511,0.847235,0.0002291372],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9241871,0.0008412537,0.001688466,0.01532023,0.0003959381,0.002928224,0.00003769815,0.0010618,0.05353926],"genre_scores_gemma":[0.970547,0.0003198885,0.006764077,0.003708986,0.0002382696,0.0008396372,0.00186458,0.00005972007,0.0156578],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1470927,"threshold_uncertainty_score":0.998552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0131525494700643,"score_gpt":0.2749567502363127,"score_spread":0.2618042007662484,"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."}}