{"id":"W3045793201","doi":"10.1109/access.2020.3012143","title":"A Stacking Ensemble Model to Predict Daily Number of Hospital Admissions for Cardiovascular Diseases","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Key Research and Development Program of Sichuan Province; National Natural Science Foundation of China","keywords":"Random forest; Mean squared error; Mean absolute percentage error; Support vector machine; Gradient boosting; Stacking; Decision tree; Lasso (programming language); Linear regression; Computer science; Statistics; Mean absolute error; Artificial intelligence; Feature selection; Machine learning; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.00003811644,0.0001301376,0.0002456844,0.00002621055,0.00004689051,0.00004228591,0.000266246,0.00004397483,0.00001646336],"category_scores_gemma":[0.0001019143,0.0001283733,0.0002600772,0.0001677199,0.000007357141,0.0002379768,0.0000517888,0.0000618794,0.000003846222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000155122,"about_ca_system_score_gemma":0.0000351106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001263421,"about_ca_topic_score_gemma":0.000001716691,"domain_scores_codex":[0.9992427,0.000006934022,0.0001801943,0.0001810577,0.0001680239,0.0002211549],"domain_scores_gemma":[0.9993879,0.00004154722,0.00002068207,0.0001941213,0.00005513391,0.0003006287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001374216,0.00001623015,0.001989615,0.0001986028,0.0002100377,0.000004011068,0.0005174977,0.9883893,0.0007447446,0.00006729002,0.004931682,0.002917315],"study_design_scores_gemma":[0.000637432,0.00008151324,0.0002603525,0.0001965693,0.0002362345,0.000001312513,0.00005629026,0.9693241,0.02104113,0.0003713366,0.007373982,0.0004197685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5432991,0.0004504237,0.453607,0.00007895312,0.0004963396,0.0002609113,0.0002096875,0.0002558813,0.001341679],"genre_scores_gemma":[0.9969992,0.00001936314,0.002485013,0.00008783582,0.0002707109,0.0000574834,0.00001299233,0.00004606349,0.00002134698],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4537,"threshold_uncertainty_score":0.5234911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0306794009126727,"score_gpt":0.2713301423786237,"score_spread":0.240650741465951,"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."}}