{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00249286,0.0003185937,0.0004434993,0.00194863,0.0004145109,0.0008828915,0.0006114061,0.0005052745,0.0009122203],"category_scores_gemma":[0.008313785,0.0003836597,0.001116905,0.002110465,0.000423159,0.001180899,0.000823598,0.001100744,0.0002322358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005609866,"about_ca_system_score_gemma":0.0007222814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005967548,"about_ca_topic_score_gemma":0.006586501,"domain_scores_codex":[0.9985117,0.0004705609,0.0002984441,0.0002231628,0.0003282779,0.000167857],"domain_scores_gemma":[0.9926136,0.003587076,0.002210315,0.0005461214,0.0006540131,0.0003889555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001236771,0.0001590045,0.9986185,0.00001079749,0.00005596689,0.00003860988,0.0000693821,0.00007191674,0.00003218816,0.00001536295,0.00005705311,0.0007475672],"study_design_scores_gemma":[0.00001471627,0.0002873772,0.9964832,0.00001549557,0.0000569455,0.0002076697,0.0005293652,0.002162773,0.00007977409,0.00003985295,0.0001128267,0.000009980098],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991361,0.00006713987,0.0001762824,0.00002814786,0.000003159916,0.00002562809,0.0004368871,0.000003740774,0.0001228002],"genre_scores_gemma":[0.9987965,0.00007873508,0.0002193335,0.00002108611,0.000008051653,0.00003603256,0.0007844233,0.000002577744,0.00005321897],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005967548,"threshold_uncertainty_score":0.01318365,"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."}}