{"id":"W4317039786","doi":"10.18280/ria.360602","title":"A Novel Ensemble Deep Learning Model for Coronary Heart Disease Prediction","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Framingham Risk Score; Ensemble learning; Framingham Heart Study; Coronary heart disease; Recall; Artificial intelligence; Blood pressure; Machine learning; Deep learning; F1 score; Disease; Heart disease; Computer science; Medicine; Internal medicine; Ensemble forecasting; Cardiology; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007280011,0.000704511,0.001028667,0.0007066005,0.0002782471,0.0005171536,0.001216295,0.0009127202,0.001239236],"category_scores_gemma":[0.001070842,0.0003158215,0.0006989234,0.0006880974,0.0001498447,0.0007416578,0.0006030253,0.001119637,0.0004226187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005215891,"about_ca_system_score_gemma":0.0008371933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01694334,"about_ca_topic_score_gemma":0.02165175,"domain_scores_codex":[0.9997628,0.00003717693,0.00001487356,0.00007352044,0.00005794521,0.00005367597],"domain_scores_gemma":[0.9996786,0.0001006357,0.00002791617,0.00002257678,0.0001459275,0.00002432998],"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":[0.0003011207,0.000369818,0.01461359,0.00006272476,0.000274714,0.0002189459,0.0000683256,0.6580374,0.002841512,0.001929062,0.01100175,0.3102811],"study_design_scores_gemma":[0.000004449557,0.00002191705,0.0004678524,0.000005638183,0.00001629042,0.00001839637,0.000002336553,0.9985822,0.0001689335,0.000470372,0.0002377421,0.000003826806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2418625,0.006777597,0.7371083,0.001681357,0.0005310958,0.00009400974,0.002013987,0.002748253,0.007182903],"genre_scores_gemma":[0.9389654,0.001246775,0.05015434,0.0005556546,0.0001458894,0.0001085093,0.001982216,0.00004492573,0.006796411],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01694334,"threshold_uncertainty_score":0.03368944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1831077181684099,"score_gpt":0.4189304730660393,"score_spread":0.2358227548976294,"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."}}