{"id":"W2529643570","doi":"10.1038/srep34540","title":"Beatquency domain and machine learning improve prediction of cardiovascular death after acute coronary syndrome","year":2016,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Heart Rate Variability and Autonomic Control","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Gilead Sciences","keywords":"Heart rate variability; Myocardial infarction; Medicine; Cardiology; Frequency domain; Acute coronary syndrome; Internal medicine; Machine learning; Artificial intelligence; Heart rate; Computer science; Blood pressure","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":[0.004852628,0.0006728871,0.0008543049,0.0008697763,0.0001808052,0.0009113911,0.000449574,0.0007752358,0.001074011],"category_scores_gemma":[0.01307266,0.0001436745,0.0006687808,0.0005649229,0.0002505178,0.000971447,0.000611391,0.001311946,0.0004043269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002582284,"about_ca_system_score_gemma":0.0004154966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00168967,"about_ca_topic_score_gemma":0.001445929,"domain_scores_codex":[0.9986744,0.0007211702,0.0001008959,0.000266526,0.0001662782,0.00007078447],"domain_scores_gemma":[0.9936361,0.004259362,0.0008201516,0.0005601366,0.0004765585,0.0002477242],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00593054,0.001692685,0.4772273,0.0002931923,0.001711593,0.0001422854,0.0001188753,0.1124607,0.005573969,0.0005668427,0.005506885,0.3887751],"study_design_scores_gemma":[0.0002994618,0.001723469,0.4231949,0.00008861661,0.0003588432,0.000184917,0.00008237367,0.5654937,0.003498023,0.003053745,0.001941209,0.00008080955],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9742023,0.003558554,0.01704673,0.001078887,0.0001829497,0.00007074283,0.001125396,0.0003744248,0.002359912],"genre_scores_gemma":[0.9888398,0.0005602468,0.008108199,0.0001981709,0.0002000989,0.00002688461,0.001602843,0.00002506976,0.0004386004],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004852628,"threshold_uncertainty_score":0.02566344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009094352970190019,"score_gpt":0.2090549415527296,"score_spread":0.1999605885825396,"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."}}