{"id":"W4385693906","doi":"10.1155/2023/6442756","title":"Towards Diagnostic Aided Systems in Coronary Artery Disease Detection: A Comprehensive Multiview Survey of the State of the Art","year":2023,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"CAD; Support vector machine; Computer science; Machine learning; Artificial intelligence; Field (mathematics); Random forest; Artificial neural network; Coronary artery disease; Feature extraction; Data mining; Data extraction; Pattern recognition (psychology); Medicine; MEDLINE; Mathematics; Internal medicine; Engineering drawing","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":[0.002657807,0.0002201653,0.0006284567,0.0003805762,0.0001646714,0.0000212713,0.001179693,0.0001154163,0.0000384916],"category_scores_gemma":[0.004908734,0.0001348439,0.0002853156,0.0007823348,0.0002013614,0.0001580852,0.0003055274,0.0008268937,0.0001127999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005982253,"about_ca_system_score_gemma":0.0009484946,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009146815,"about_ca_topic_score_gemma":0.00428609,"domain_scores_codex":[0.9916123,0.003277421,0.003119345,0.0002142854,0.001420042,0.0003566797],"domain_scores_gemma":[0.9876502,0.00542558,0.002523319,0.0004493939,0.003792182,0.0001593124],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007350236,0.0002200042,0.9160132,0.001199282,0.0003803982,0.0001449777,0.004772768,0.06827187,0.0006654608,0.0002599203,0.001834726,0.005502368],"study_design_scores_gemma":[0.0003229103,0.0001155709,0.9615315,0.009507188,0.00003602101,0.00007113234,0.005671325,0.01919356,0.0009559675,0.0003608133,0.002069757,0.0001643066],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9723242,0.002427988,0.0008793735,0.0008835783,0.02130332,0.001748452,0.0003580143,0.00001866782,0.00005642382],"genre_scores_gemma":[0.99817,0.0008714113,0.000003289587,0.0001210092,0.0003130015,0.00008821185,0.000009903139,0.00003010298,0.0003930358],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04907831,"threshold_uncertainty_score":0.9974514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1777750414220074,"score_gpt":0.4422912322792878,"score_spread":0.2645161908572805,"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."}}