{"id":"W3117642670","doi":"10.1109/iemcon51383.2020.9284951","title":"ECG Knowledge Discovery Based on Ontologies and Rules Learning for the Support of Personalized Medical Decision Making","year":2020,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Knowledge extraction; Knowledge base; Decision support system; Classifier (UML); Process (computing); Knowledge representation and reasoning; Artificial intelligence; Machine learning; Knowledge-based systems; Data mining; Data science","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":[0.00288878,0.0003403364,0.0006356497,0.00232481,0.0008357344,0.002240113,0.001088939,0.0006763084,0.001136763],"category_scores_gemma":[0.01035846,0.0003015726,0.001165359,0.0021542,0.0009116394,0.002866239,0.001601178,0.001183125,0.0003990386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001149108,"about_ca_system_score_gemma":0.002564609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007262341,"about_ca_topic_score_gemma":0.007805819,"domain_scores_codex":[0.997414,0.0007855764,0.0003440524,0.0004330226,0.0009051976,0.0001181329],"domain_scores_gemma":[0.9958401,0.00215564,0.0003510818,0.0006706517,0.0008604294,0.0001221766],"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":[0.000185262,0.0004488974,0.008294648,0.0005293143,0.0003585643,0.001013512,0.001244331,0.09967476,0.01114439,0.2782162,0.007496886,0.5913932],"study_design_scores_gemma":[0.00004011691,0.0000629363,0.001913217,0.0002211446,0.0002028776,0.0003452407,0.0003031388,0.7409602,0.007912009,0.2192441,0.02874824,0.00004693185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01660761,0.0005968309,0.974587,0.0009487227,0.00006342646,0.0001926308,0.0003289114,0.0006082959,0.006066593],"genre_scores_gemma":[0.2436206,0.0007737528,0.7524929,0.0003004652,0.00007159836,0.0001782426,0.0009434689,0.00006521219,0.001553718],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007262341,"threshold_uncertainty_score":0.0152775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03388454777414192,"score_gpt":0.313301576427752,"score_spread":0.2794170286536101,"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."}}