{"id":"W4390788709","doi":"10.3390/biomedinformatics4010014","title":"Factors Associated with Unplanned Hospital Readmission after Discharge: A Descriptive and Predictive Study Using Electronic Health Record Data","year":2024,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada; Fondation de la recherche en santé du Nouveau-Brunswick","keywords":"Medicine; Emergency medicine; Hospital readmission; Hospital discharge; Comorbidity; Health care; Descriptive statistics; Electronic health record; Medical emergency; Intensive care medicine; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007587042,0.0002548917,0.0002910452,0.0002648087,0.0002149314,0.0003739904,0.0006543898,0.00007759973,0.000004957576],"category_scores_gemma":[0.0001985573,0.000172903,0.00002079274,0.0007864305,0.00005657462,0.001740452,0.0006230756,0.0004189232,0.000002342256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004618507,"about_ca_system_score_gemma":0.0006861034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001374399,"about_ca_topic_score_gemma":0.00009355432,"domain_scores_codex":[0.9979125,0.0001428768,0.000443571,0.0004223681,0.0005102959,0.0005684496],"domain_scores_gemma":[0.9986201,0.0001656278,0.0002184697,0.0006468307,0.00008082619,0.0002681315],"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":[0.0001721702,0.0008488992,0.4827032,0.001526768,0.001089121,0.00008399439,0.4561988,0.0000631139,0.00001022354,0.0007776329,0.004125501,0.05240058],"study_design_scores_gemma":[0.0004423405,0.004049174,0.06524882,0.0007527671,0.00004351373,0.000009078425,0.007205627,0.9205372,0.000003590398,0.0001212276,0.001221437,0.0003652616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9145344,0.0004617121,0.08188504,0.0008448942,0.0005996647,0.001051717,0.0002234517,0.0003790128,0.00002007101],"genre_scores_gemma":[0.996547,0.00002535338,0.003045539,0.00006723608,0.00004547983,0.00001725372,0.0001986197,0.00002142533,0.00003209662],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9204741,"threshold_uncertainty_score":0.7050781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05943633180117005,"score_gpt":0.3244137911842424,"score_spread":0.2649774593830724,"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."}}