{"id":"W4409229190","doi":"10.1016/j.bspc.2025.107824","title":"Attention-based hybrid deep learning models and its scientific validation for cardiovascular disease risk stratification","year":2025,"lang":"en","type":"article","venue":"Biomedical Signal Processing and Control","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University; University of Toronto","funders":"","keywords":"Risk stratification; Stratification (seeds); Computer science; Artificial intelligence; Disease; Machine learning; Deep learning; Medicine; Cardiology; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004678404,0.0006550908,0.0006192417,0.001156045,0.0004053632,0.001089872,0.001223095,0.001490209,0.001514587],"category_scores_gemma":[0.01197144,0.0002418009,0.0007326807,0.0005931614,0.0004527809,0.001249643,0.001322971,0.001455956,0.0003594077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007377723,"about_ca_system_score_gemma":0.001195713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007567172,"about_ca_topic_score_gemma":0.004961692,"domain_scores_codex":[0.9992946,0.0002891337,0.00003960472,0.0001614142,0.0001366681,0.00007864914],"domain_scores_gemma":[0.9956955,0.002379292,0.0002587145,0.0003742836,0.001140695,0.0001515011],"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":[0.0009755358,0.000650044,0.03131166,0.0002938915,0.0007898525,0.0002192612,0.0001883084,0.5482763,0.01039206,0.01566843,0.01084947,0.3803852],"study_design_scores_gemma":[0.000009743522,0.00003722599,0.001690665,0.00001787008,0.00004150242,0.00001646406,0.000008297082,0.9930976,0.0009461176,0.003818081,0.0003088618,0.000007519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3858982,0.005468908,0.5959546,0.003081479,0.0004842415,0.0001003563,0.001234629,0.001479343,0.006298206],"genre_scores_gemma":[0.9728112,0.0003706905,0.02367827,0.0002601416,0.0001123234,0.0000457834,0.0007497386,0.00004103082,0.001930821],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007567172,"threshold_uncertainty_score":0.02474201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07233119977492414,"score_gpt":0.3812535670607506,"score_spread":0.3089223672858265,"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."}}