{"id":"W2107373858","doi":"10.2139/ssrn.215376","title":"Inferring Hospital Quality from Patient Discharge Records Using a Bayesian Selection Model","year":2000,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Model selection; Bayesian probability; Hospital discharge; Quality (philosophy); Selection (genetic algorithm); Bayesian inference; Medicine; Data mining; Computer science; Medical emergency; Emergency medicine; Statistics; Artificial intelligence; Intensive care medicine; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00890746,0.0007655808,0.001853911,0.003345811,0.0005534234,0.001995974,0.002064314,0.001921373,0.002043107],"category_scores_gemma":[0.04281392,0.001024395,0.002161183,0.002851774,0.0006993118,0.002229729,0.00111743,0.001982692,0.0006267638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001290974,"about_ca_system_score_gemma":0.002002467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01903816,"about_ca_topic_score_gemma":0.01734416,"domain_scores_codex":[0.995987,0.00196962,0.000313317,0.0008018056,0.0006425409,0.0002856571],"domain_scores_gemma":[0.9633765,0.03204466,0.002012439,0.000846705,0.00129727,0.0004225202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001858545,0.001242873,0.2729578,0.000293946,0.001368646,0.0004722963,0.0006014424,0.5242691,0.001660787,0.02104077,0.005498682,0.1687351],"study_design_scores_gemma":[0.0001773952,0.0001040031,0.01772271,0.00003118099,0.000171012,0.00009311988,0.00004245645,0.9664643,0.0003717336,0.01443602,0.0003474236,0.00003869259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3376837,0.0006943879,0.654033,0.002796841,0.00007698509,0.0002266543,0.00221181,0.0006379121,0.001638831],"genre_scores_gemma":[0.9252912,0.0004522582,0.06977455,0.0003024653,0.0001468828,0.0002087022,0.002380691,0.00004825582,0.001394875],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01903816,"threshold_uncertainty_score":0.0471077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01448060006188077,"score_gpt":0.2915003019860473,"score_spread":0.2770197019241665,"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."}}