{"id":"W2341856726","doi":"10.4236/jbise.2016.95019","title":"Detection of Ventricular Fibrillation Using Random Forest Classifier","year":2016,"lang":"en","type":"article","venue":"Journal of Biomedical Science and Engineering","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Random forest; Ventricular fibrillation; Ventricular tachycardia; Computer science; Artificial intelligence; Sliding window protocol; Pattern recognition (psychology); Segmentation; Classifier (UML); Feature (linguistics); Fibrillation; Window (computing); Cardiology; Atrial fibrillation; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006025939,0.00003591923,0.0001370679,0.0002704026,0.00003035809,0.000007062326,0.0000312921,0.00003001946,0.000002511259],"category_scores_gemma":[0.000485074,0.00001939115,0.00004861585,0.0004364865,0.0001031293,0.0001251515,0.00001019256,0.00004445082,1.924111e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000577625,"about_ca_system_score_gemma":0.00006395487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005062841,"about_ca_topic_score_gemma":1.165036e-7,"domain_scores_codex":[0.9992348,0.000003765197,0.0001978735,0.00005184796,0.0004171715,0.00009452524],"domain_scores_gemma":[0.9995559,0.000040992,0.00008705152,0.0000380223,0.0001418349,0.0001361768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000214735,0.000007056774,0.01035856,0.00002123929,0.00001728433,0.00001400586,0.00001406183,0.0001860192,0.9319059,0.000002125318,0.000002135964,0.05745009],"study_design_scores_gemma":[0.005937275,0.001065112,0.2674968,0.002713788,0.000478079,0.001845755,0.0001229221,0.3800575,0.3334808,0.00007965434,0.006474329,0.0002479733],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8979589,0.0001834431,0.1015087,0.0001619614,0.0001651734,0.00001426692,1.489945e-7,0.000003070465,0.000004356522],"genre_scores_gemma":[0.9985498,0.00005901908,0.00107384,0.000002763624,0.0003072123,6.826043e-8,3.474459e-8,0.000002110553,0.000005120258],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5984251,"threshold_uncertainty_score":0.07907483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01188909215617489,"score_gpt":0.2494409231597451,"score_spread":0.2375518310035702,"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."}}