{"id":"W2794810467","doi":"","title":"Hospital Readmission is Highly Predictable from Deep Learning","year":2017,"lang":"en","type":"article","venue":"Cahiers de recherche","topic":"Heart Failure Treatment and Management","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Receiver operating characteristic; Hospital readmission; Reliability (semiconductor); Medicine; Hospital discharge; Hospital admission; Machine learning; Random forest; Emergency medicine; Acute care; Discharge planning; Health care; Artificial intelligence; Medical emergency; Computer science; Intensive care medicine; Internal medicine; Nursing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002617163,0.000497204,0.0004793265,0.0008573454,0.0002975556,0.001065188,0.000553684,0.0006270957,0.001773127],"category_scores_gemma":[0.01923354,0.0002874383,0.0004009417,0.0007089299,0.0004242104,0.000775227,0.0006153748,0.001170954,0.0005282228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001998982,"about_ca_system_score_gemma":0.001859715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1013207,"about_ca_topic_score_gemma":0.1567855,"domain_scores_codex":[0.9990374,0.0003545128,0.00006723133,0.0001879908,0.0001670668,0.0001857482],"domain_scores_gemma":[0.9893349,0.006187866,0.002011277,0.0007074047,0.001283876,0.0004746977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002162272,0.0001313556,0.9137695,0.00005064287,0.0001651312,0.0002009243,0.00007767413,0.04838563,0.0003871263,0.0008650153,0.004710065,0.03104076],"study_design_scores_gemma":[0.00002245018,0.000123311,0.5388088,0.00007651913,0.00006320988,0.0001662401,0.0001538828,0.4530473,0.0005309955,0.005617714,0.001346136,0.00004338973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9675325,0.0008985294,0.01875421,0.004657259,0.00008355346,0.00006032645,0.004120342,0.0003485301,0.003544818],"genre_scores_gemma":[0.9967818,0.000120113,0.001015451,0.0001804181,0.00002130658,0.00001151521,0.001153049,0.00001308174,0.0007032902],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1013207,"threshold_uncertainty_score":0.201462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0821696462942282,"score_gpt":0.3558678611524684,"score_spread":0.2736982148582403,"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."}}