{"id":"W2004195880","doi":"10.1109/icmla.2011.115","title":"Predicting Patients Likely to Overstay in Hospitals","year":2011,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Economic shortage; Schedule; Decision tree; Medicine; Medical emergency; Operations management; Computer science; Artificial intelligence; Engineering","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":[0.0002257305,0.00008027802,0.0000902959,0.0001225976,0.00004205303,0.00003144542,0.0005634189,0.00003836409,0.00006858938],"category_scores_gemma":[0.0001994098,0.00007326429,0.00001947961,0.0003373062,0.000006241488,0.0002706164,0.0003161315,0.0001423038,0.0001389328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005451851,"about_ca_system_score_gemma":0.00002713589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002552939,"about_ca_topic_score_gemma":0.0002243622,"domain_scores_codex":[0.9989727,0.00006053253,0.0001958475,0.0002977278,0.0002025778,0.0002706811],"domain_scores_gemma":[0.9993666,0.00004665856,0.00004419975,0.0003633406,0.00005545792,0.0001236972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000001924143,0.00002584875,0.979779,0.000006922581,9.309336e-7,0.000002427211,0.004467903,0.00001853632,0.000001080823,0.005742401,0.0001902738,0.009762767],"study_design_scores_gemma":[0.0001468139,0.00022161,0.9884769,0.00002158718,3.132926e-7,4.177146e-7,0.00003259658,0.01013328,0.00006501588,0.0004883773,0.0003136747,0.00009936705],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.94478,0.000006615121,0.02421612,0.0004227587,0.0003795136,0.0002433719,6.592601e-7,0.0002068081,0.02974415],"genre_scores_gemma":[0.9499307,5.350976e-7,0.04895102,0.0008648395,0.00001757663,0.00001127425,4.935376e-7,0.000006256873,0.0002173372],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02952682,"threshold_uncertainty_score":0.3859297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0184135447139406,"score_gpt":0.2578466514008341,"score_spread":0.2394331066868935,"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."}}