{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00167705,0.0002981628,0.0003920158,0.0002156704,0.005011301,0.00002167951,0.0004846378,0.000153482,0.001399438],"category_scores_gemma":[0.0009463238,0.0003455904,0.0002277633,0.000542301,0.0001156286,0.0002222967,0.000412126,0.001442627,0.0005320192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006477127,"about_ca_system_score_gemma":0.0005356008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002437298,"about_ca_topic_score_gemma":0.0001874968,"domain_scores_codex":[0.9958248,0.0004430114,0.00130038,0.0008518614,0.0004610316,0.001118907],"domain_scores_gemma":[0.9968258,0.001285587,0.0003543548,0.0006866588,0.0004190734,0.0004285285],"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":[0.0005730971,0.000297909,0.01716408,0.0003438758,0.00001468842,0.000006419191,0.009965587,0.947115,0.004461345,0.008061357,0.002652771,0.009343867],"study_design_scores_gemma":[0.00007586757,0.0002959355,0.0002591077,0.00009974902,0.00003286905,0.00001082914,0.01677813,0.9515442,0.0004503108,0.008259704,0.0218933,0.0003000318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09510769,0.0006810703,0.8919224,0.004451732,0.002088168,0.003711153,0.0003200722,0.0005122722,0.001205515],"genre_scores_gemma":[0.9764844,0.00005342901,0.003333996,0.001796813,0.000429119,0.003630096,0.0001959135,0.0001039595,0.01397226],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8885883,"threshold_uncertainty_score":0.9998996,"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."}}