{"id":"W2730496402","doi":"10.1109/access.2017.2723258","title":"Life-Threatening Ventricular Arrhythmia Detection With Personalized Features","year":2017,"lang":"en","type":"article","venue":"IEEE Access","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; QRS complex; Feature (linguistics); Pattern recognition (psychology); Support vector machine; Artificial intelligence; Feature extraction; Area under curve; Data mining; Medicine; Cardiology; Internal medicine","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":[0.0005663427,0.0006506394,0.000846976,0.001127851,0.000174681,0.0006370191,0.0004148147,0.0005046648,0.0005388981],"category_scores_gemma":[0.002713713,0.0001259937,0.000496892,0.0008316368,0.0001141723,0.0008262923,0.0005203342,0.0004033412,0.0004213501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001647167,"about_ca_system_score_gemma":0.0002022305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006606719,"about_ca_topic_score_gemma":0.0007998999,"domain_scores_codex":[0.9991332,0.00012413,0.00009495827,0.0002267743,0.0003136292,0.0001074223],"domain_scores_gemma":[0.9990847,0.0002679422,0.0001601165,0.000194941,0.0002339937,0.00005826563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000912059,0.0004118903,0.07250194,0.0001998406,0.0002794215,0.0006603053,0.00007851152,0.02623357,0.05974186,0.0003163634,0.005523439,0.8331408],"study_design_scores_gemma":[0.000112436,0.001601965,0.2750332,0.000058331,0.0004194162,0.003738606,0.0002115202,0.6318843,0.07576454,0.001909704,0.009093615,0.0001723961],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8462381,0.00198625,0.1442177,0.0002191607,0.0001724356,0.0001592879,0.001736345,0.002807913,0.002462855],"genre_scores_gemma":[0.9680416,0.0002321475,0.02856559,0.00005824887,0.00007231988,0.0000540294,0.002291556,0.00002730812,0.0006572469],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001127851,"threshold_uncertainty_score":0.002995193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02925472666333087,"score_gpt":0.325686005694545,"score_spread":0.2964312790312141,"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."}}