{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007276345,0.0002442957,0.0003049065,0.001683898,0.0003438028,0.0007254381,0.0002548387,0.000484712,0.001226213],"category_scores_gemma":[0.006526706,0.000110662,0.000323233,0.0009093687,0.0001246185,0.0005189934,0.0003815337,0.0004165086,0.0003113306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003344931,"about_ca_system_score_gemma":0.0004471741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005852729,"about_ca_topic_score_gemma":0.007625532,"domain_scores_codex":[0.9996051,0.00009037657,0.00007356959,0.00005630779,0.00008478846,0.00008982568],"domain_scores_gemma":[0.9948695,0.00239301,0.001446549,0.0001190915,0.0006202872,0.0005515781],"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":[0.0001226163,0.00007308833,0.990621,0.000007262754,0.00001018606,0.0001066843,0.00005675559,0.0008866442,0.000269846,0.00001718439,0.0002914705,0.007537238],"study_design_scores_gemma":[0.0000174868,0.0003259148,0.9558564,0.00002219387,0.00002603668,0.0004700312,0.0008427003,0.04101349,0.0008370643,0.0001465228,0.000425523,0.00001665547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999248,0.00003290048,0.0002858515,0.00007399763,0.000005647206,0.00001022803,0.0001307176,0.00001033169,0.0002023271],"genre_scores_gemma":[0.9987358,0.00003085888,0.0007044109,0.0000206965,0.000009505354,0.0000054056,0.0003701627,0.000001574533,0.000121445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005852729,"threshold_uncertainty_score":0.01163733,"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."}}