{"id":"W2122098984","doi":"10.5539/cis.v5n5p35","title":"Characterization of Ventricular Tachycardia and Fibrillation Using Semantic Mining","year":2012,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universiti Teknologi Malaysia","keywords":"Ventricular tachycardia; Normal Sinus Rhythm; Computer science; Ventricular fibrillation; Sensitivity (control systems); SIGNAL (programming language); Sinus rhythm; Pattern recognition (psychology); Cardiology; Tachycardia; Internal medicine; Artificial intelligence; Atrial fibrillation; Medicine; Electronic engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005716052,0.0004395883,0.0004883473,0.003106336,0.0003231637,0.0006759366,0.0004144895,0.0005832587,0.0005063385],"category_scores_gemma":[0.002153492,0.0001184297,0.0007688078,0.00148012,0.0002234609,0.0009597419,0.0004376036,0.0002472899,0.0003039577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002386773,"about_ca_system_score_gemma":0.0004828044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008445869,"about_ca_topic_score_gemma":0.001154758,"domain_scores_codex":[0.9992982,0.000146825,0.0001389581,0.0001486074,0.0002140318,0.00005341363],"domain_scores_gemma":[0.9991009,0.0003027765,0.0002186972,0.00009641118,0.0002427073,0.00003852413],"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":[0.0007386762,0.0005343818,0.06830703,0.0005020869,0.0002790299,0.001382816,0.000418931,0.02404134,0.08273551,0.005514125,0.00377519,0.811771],"study_design_scores_gemma":[0.0001054106,0.0005614628,0.08455278,0.0001437478,0.0003757825,0.006222448,0.0006880386,0.7998835,0.06371826,0.02615131,0.01745381,0.0001434331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3848647,0.001266157,0.6046346,0.0004415156,0.0000975137,0.0002666361,0.002089515,0.001466585,0.004872913],"genre_scores_gemma":[0.8385486,0.000358803,0.1575585,0.00008263056,0.00005900454,0.00009725819,0.002671772,0.00003988086,0.0005835611],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003106336,"threshold_uncertainty_score":0.003022969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01741876216139937,"score_gpt":0.2677857022079605,"score_spread":0.2503669400465611,"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."}}