{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.001086911,0.0003836093,0.0004542886,0.0004768961,0.000252857,0.0005302466,0.00748179,0.0003520805,0.00007941108],"category_scores_gemma":[0.003223646,0.0003586131,0.0001057972,0.0009354412,0.0000252396,0.0001771616,0.03201054,0.001473298,0.0003387353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001879138,"about_ca_system_score_gemma":0.001443917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003952953,"about_ca_topic_score_gemma":0.0008201206,"domain_scores_codex":[0.9957571,0.0002235205,0.000630165,0.00193997,0.0008351561,0.0006141143],"domain_scores_gemma":[0.990473,0.0002882195,0.000255051,0.007909089,0.0002248957,0.000849699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001266193,0.0003093415,0.09490062,0.001593944,0.0003752413,0.0002896163,0.005185722,0.3534738,0.00003663164,0.006549336,0.4629528,0.07432034],"study_design_scores_gemma":[0.00006924158,0.00005395242,0.005596904,0.0004392486,0.00002863156,0.000005876846,0.00003217236,0.9434126,0.00000357175,0.002285497,0.04752526,0.0005470348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003144253,0.00005770961,0.962305,0.0238421,0.007002807,0.0006863883,0.0002046192,0.001551653,0.001205445],"genre_scores_gemma":[0.07215675,0.00004945321,0.9155768,0.00132364,0.001716677,0.00002816101,0.0005960498,0.0001088067,0.008443678],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5899388,"threshold_uncertainty_score":0.9998866,"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."}}