{"id":"W2980705747","doi":"10.1177/0272989x19879095","title":"Probabilistic Graphical Modeling for Estimating Risk of Coronary Artery Disease: Applications of a Flexible Machine-Learning Method","year":2019,"lang":"en","type":"article","venue":"Medical Decision Making","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Public Health Ontario; University of Toronto; University of Calgary","funders":"","keywords":"Machine learning; Bayesian network; Artificial intelligence; Coronary artery disease; Missing data; CAD; Computer science; Receiver operating characteristic; Probabilistic logic; Logistic regression; Risk assessment; Artificial neural network; Support vector machine; Medicine; Internal medicine; Engineering","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":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004491739,0.0001834959,0.0005637986,0.000239252,0.0005397042,0.000005782838,0.0004301548,0.000309891,0.0009877101],"category_scores_gemma":[0.01371801,0.0001550808,0.0001992968,0.0004830535,0.0001014901,0.00008529495,0.0002613766,0.001121034,0.00005442778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008232872,"about_ca_system_score_gemma":0.0004837415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001640003,"about_ca_topic_score_gemma":0.00006422854,"domain_scores_codex":[0.9953601,0.0008254893,0.001782698,0.0005087701,0.001024478,0.0004984344],"domain_scores_gemma":[0.9827093,0.01514435,0.0007050024,0.0005324092,0.0005789434,0.0003299672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008488218,0.0001952615,0.2264956,0.002423411,0.00005676602,0.000005959975,0.001693292,0.1405438,0.00004079989,0.01764614,0.00005571939,0.6099944],"study_design_scores_gemma":[0.0002247796,0.0001104727,0.00143284,0.002295471,0.00005435077,0.000002315067,0.0008700375,0.910576,0.000003227752,0.08414912,0.0001597619,0.0001215971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1819129,0.0003439834,0.814739,0.0001718635,0.0004623075,0.002112559,0.00005592192,0.00008014114,0.0001212391],"genre_scores_gemma":[0.7651165,0.00002259335,0.2339574,0.0001160477,0.0001569662,0.0005273945,0.0000220468,0.00003570379,0.00004530204],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7700322,"threshold_uncertainty_score":0.9999255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.10613431735222,"score_gpt":0.5019966610811613,"score_spread":0.3958623437289413,"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."}}