{"id":"W4372311241","doi":"10.1016/j.dajour.2023.100242","title":"A novel machine learning model with Stacking Ensemble Learner for predicting emergency readmission of heart-disease patients","year":2023,"lang":"en","type":"article","venue":"Decision Analytics Journal","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":104,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Ryerson University","keywords":"Machine learning; Artificial intelligence; Ensemble learning; Heart disease; Computer science; Emergency department; Disease; Ensemble forecasting; Robustness (evolution); Overcrowding; Novelty; Medical emergency; Medicine; Cardiology; Internal medicine; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":["sts"],"consensus_categories":[],"category_scores_codex":[0.002249255,0.000210807,0.0004036099,0.0005253634,0.001757113,0.00002036259,0.0002513679,0.0001622529,0.0002205283],"category_scores_gemma":[0.005830155,0.0001612045,0.0001697214,0.0007685453,0.00003341828,0.0002133037,0.0001488714,0.00120558,0.00005320381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001717524,"about_ca_system_score_gemma":0.0006597374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007790971,"about_ca_topic_score_gemma":0.0001721988,"domain_scores_codex":[0.9962485,0.0002137238,0.001529576,0.0003391596,0.000966855,0.0007021771],"domain_scores_gemma":[0.995162,0.001103637,0.0008930641,0.000298912,0.001907771,0.0006346136],"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":[0.001207737,0.0001328453,0.7435713,0.0001955673,0.00003257851,0.000007114959,0.00214975,0.2382515,0.0004676855,0.0001722837,0.006488116,0.007323535],"study_design_scores_gemma":[0.0006712464,0.0003063094,0.009645486,0.000974633,0.00006136986,0.000002687726,0.001811457,0.9793078,0.00004386415,0.004855139,0.002144534,0.0001754434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7038029,0.00009115438,0.2934989,0.001032733,0.0006091064,0.0006628321,0.0000740507,0.00008873658,0.0001396223],"genre_scores_gemma":[0.9851325,0.0002295264,0.0121488,0.0002140144,0.0002650854,0.00002363614,0.00005200729,0.00008046353,0.001853994],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7410564,"threshold_uncertainty_score":0.9995425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2120780156338272,"score_gpt":0.4805615421646227,"score_spread":0.2684835265307955,"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."}}