{"id":"W4386707539","doi":"10.32920/24132876","title":"Using Big Data &amp; Analytics to Predict Hospital Re-Admissions","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Canadian Institute for Health Information","funders":"","keywords":"Big data; Terabyte; Volume (thermodynamics); Computer science; Variety (cybernetics); Data science; Analytics; Data mining; Operating system; Artificial intelligence","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":[0.002352177,0.000987942,0.000551123,0.003690377,0.0004711662,0.002849323,0.0007073777,0.001064577,0.005399254],"category_scores_gemma":[0.0181946,0.0003935052,0.001029811,0.002940509,0.0003210286,0.002075019,0.001220717,0.001569973,0.00306793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009731914,"about_ca_system_score_gemma":0.001718169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01954031,"about_ca_topic_score_gemma":0.03265,"domain_scores_codex":[0.9987448,0.000550911,0.00008811671,0.000193381,0.0002818415,0.0001409178],"domain_scores_gemma":[0.9935954,0.003551248,0.0007220694,0.0005926392,0.0009108534,0.0006278442],"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":[0.0007243066,0.0008780084,0.5392486,0.0003126402,0.000680241,0.0003682417,0.0004365853,0.04802443,0.0004721507,0.007302513,0.1078253,0.293727],"study_design_scores_gemma":[0.0001311136,0.0003386756,0.1131983,0.000326907,0.0002176754,0.0002503225,0.001332524,0.8282249,0.001643596,0.0348205,0.01940511,0.0001103537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6832406,0.006890604,0.1195479,0.05974963,0.002924337,0.0007770478,0.05067458,0.00623899,0.06995637],"genre_scores_gemma":[0.9210691,0.001930138,0.04534804,0.001201294,0.0009516994,0.0001626217,0.02308439,0.0001713114,0.006081389],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01954031,"threshold_uncertainty_score":0.03885317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4077546698554103,"score_gpt":0.4345404228154561,"score_spread":0.02678575296004582,"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."}}