{"id":"W4390485420","doi":"10.1109/sipaim56729.2023.10373478","title":"Generative Data by β-Variational Autoencoders Help Build Stronger Classifiers: ECG Use Case","year":2023,"lang":"en","type":"article","venue":"","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian VIGOUR Centre; University of Alberta","funders":"Alberta Machine Intelligence Institute","keywords":"Normal Sinus Rhythm; Artificial intelligence; Computer science; Medical diagnosis; Pattern recognition (psychology); Abnormality; Machine learning; Autoencoder; Generative grammar; Generative model; Deep learning; Atrial fibrillation; Medicine; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005406629,0.001272771,0.001035918,0.0006924364,0.000413787,0.001287802,0.001235137,0.002070198,0.001529831],"category_scores_gemma":[0.01602533,0.00052118,0.001299964,0.0004510107,0.0009851326,0.001598341,0.001405622,0.002992013,0.0005929224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007866537,"about_ca_system_score_gemma":0.0005893622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004619301,"about_ca_topic_score_gemma":0.006170816,"domain_scores_codex":[0.9984344,0.0008607373,0.00006969755,0.0003955981,0.0001413589,0.00009811576],"domain_scores_gemma":[0.989953,0.007598332,0.0002850522,0.001264748,0.0006741103,0.0002249119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006472208,0.0004524146,0.02491268,0.0001775312,0.0004848491,0.0005181822,0.0003235764,0.8069338,0.005355167,0.01219763,0.009239283,0.1387578],"study_design_scores_gemma":[0.00002326008,0.00005088066,0.0007448419,0.00001299782,0.00002029196,0.00004936576,0.00001921422,0.9915575,0.001267819,0.005643358,0.0006016425,0.000008892327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4869024,0.003085557,0.4945977,0.005749038,0.0003360692,0.0001586498,0.001412454,0.001794853,0.005963326],"genre_scores_gemma":[0.9274193,0.0002891839,0.06738649,0.000710966,0.0001358132,0.00005028719,0.001647121,0.0001433864,0.002217492],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005406629,"threshold_uncertainty_score":0.02859336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1293727377658139,"score_gpt":0.3555843784581408,"score_spread":0.2262116406923269,"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."}}