{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002333189,0.00003087705,0.00007393426,0.0001623635,0.00007365215,0.00003265026,0.00001464675,0.00001305884,8.48099e-7],"category_scores_gemma":[0.00001633851,0.00002586478,0.00001192049,0.0002834438,0.00005153558,0.002006523,0.00002649427,0.00001471134,7.725997e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009304194,"about_ca_system_score_gemma":0.00001513289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002380626,"about_ca_topic_score_gemma":3.845376e-9,"domain_scores_codex":[0.9996269,0.000004767347,0.0001243821,0.0000369893,0.0001349301,0.00007205],"domain_scores_gemma":[0.9997331,0.00000741286,0.00007136419,0.00004875144,0.00008638752,0.00005296687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000004620681,0.000006746667,0.8556244,0.0001300527,0.00001212371,3.736265e-7,0.002959331,0.0001647659,0.02607599,0.0001413009,0.000002500729,0.1148778],"study_design_scores_gemma":[0.0001192331,0.00001955216,0.6510834,0.00006060815,0.00002500755,0.00006087467,0.00005542561,0.3423114,0.005879849,9.183746e-7,0.0003464963,0.00003725304],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9341231,0.00003607979,0.06561273,0.00001722017,0.00009706305,0.0000354853,4.804081e-7,0.000006965688,0.0000708938],"genre_scores_gemma":[0.9954804,0.00002145267,0.004354194,0.00003332714,0.0001017777,1.668206e-7,0.000006168475,7.796237e-7,0.000001711483],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3421466,"threshold_uncertainty_score":0.1454681,"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."}}