{"id":"W2551779716","doi":"10.1109/ijcnn.2016.7727866","title":"Feature leaning with deep Convolutional Neural Networks for screening patients with paroxysmal atrial fibrillation","year":2016,"lang":"en","type":"article","venue":"","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Feature extraction; Classifier (UML); Pattern recognition (psychology); Deep learning; Atrial fibrillation; Feature (linguistics); Artificial neural network; Paroxysmal atrial fibrillation; Machine learning; Cardiology; 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":[0.0004914278,0.0006151232,0.0004093709,0.0005500218,0.0001640177,0.000251714,0.0005415463,0.0005291039,0.0006487521],"category_scores_gemma":[0.001334366,0.000198758,0.0003251735,0.0003502875,0.0001458338,0.0003553124,0.0003620932,0.0005359862,0.0002002054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003780746,"about_ca_system_score_gemma":0.0004914327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004512412,"about_ca_topic_score_gemma":0.005909912,"domain_scores_codex":[0.9998063,0.00004396096,0.00001517136,0.00004333963,0.0000602391,0.0000310231],"domain_scores_gemma":[0.9997106,0.0001509487,0.00003759677,0.00001971865,0.00006402186,0.0000171618],"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":[0.0004856478,0.0003363486,0.0145765,0.0001066757,0.00009465996,0.0004141734,0.00007846838,0.117162,0.02385429,0.001051253,0.003506212,0.8383339],"study_design_scores_gemma":[0.00002180503,0.0001173583,0.004222271,0.00001250522,0.00003574611,0.0002336467,0.00001262102,0.9834735,0.009938778,0.001003571,0.0009130805,0.00001517595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1777261,0.001604513,0.8148012,0.0005697126,0.0001024802,0.0001249242,0.0003479134,0.002687564,0.002035523],"genre_scores_gemma":[0.8695039,0.0003602625,0.1278818,0.000234798,0.0000452429,0.00006784203,0.0004100998,0.00003449963,0.001461542],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004512412,"threshold_uncertainty_score":0.008972287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01119414526533147,"score_gpt":0.2339974859515652,"score_spread":0.2228033406862338,"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."}}