{"id":"W2762019301","doi":"10.1109/ihtc.2017.8058202","title":"Detection of heart abnormalities via artificial neural network: An application of self learning algorithms","year":2017,"lang":"en","type":"article","venue":"","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial neural network; Computer science; Precordial examination; SIGNAL (programming language); Process (computing); Artificial intelligence; Signal processing; Pattern recognition (psychology); Electrocardiography; Algorithm; Machine learning; Digital signal processing; Computer hardware; Medicine; Cardiology","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":[0.0008485601,0.0005221437,0.0005667115,0.0009023899,0.0002334361,0.0005801108,0.000613867,0.0008248157,0.0008880376],"category_scores_gemma":[0.001770146,0.0002299926,0.0004217656,0.0006686899,0.0002151282,0.0004983603,0.0004274146,0.0005569155,0.0003216643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002552222,"about_ca_system_score_gemma":0.0003091119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001765357,"about_ca_topic_score_gemma":0.001565598,"domain_scores_codex":[0.999576,0.0001122005,0.00003786184,0.00009821999,0.0001430314,0.00003268087],"domain_scores_gemma":[0.9994478,0.0002853144,0.00004860691,0.00003288043,0.0001703191,0.00001510194],"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":[0.0002526697,0.0003948949,0.006136423,0.0001652726,0.000149993,0.0001740841,0.0001059322,0.2204224,0.01614175,0.002006807,0.001837153,0.7522126],"study_design_scores_gemma":[0.000005903084,0.00007400367,0.0009150107,0.000009549402,0.0000134623,0.0000516718,0.000007064533,0.9952587,0.002745759,0.0004710734,0.0004399679,0.000007821769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06378604,0.001000479,0.9303828,0.0002472729,0.0001238315,0.0001322721,0.00006584157,0.001421522,0.002839819],"genre_scores_gemma":[0.6495122,0.0007008445,0.3454195,0.0001572279,0.0001062182,0.0002078522,0.0001833736,0.00007741253,0.003635403],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001765357,"threshold_uncertainty_score":0.004487693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02265740154584466,"score_gpt":0.2996133318132913,"score_spread":0.2769559302674466,"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."}}