{"id":"W4328127903","doi":"10.3390/computers12030068","title":"A Temporal Transformer-Based Fusion Framework for Morphological Arrhythmia Classification","year":2023,"lang":"en","type":"article","venue":"Computers","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Athabasca University","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Convolutional neural network; Raw data; Encoder; Cardiac arrhythmia; Feature learning; Transformer; Pattern recognition (psychology); Recurrent neural network; Data mining; Machine learning; Artificial neural network; Atrial fibrillation; Engineering; Medicine; Cardiology","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":[0.0005146828,0.0005091924,0.0005921738,0.0007668019,0.0002504636,0.0006139009,0.0009802973,0.0005494543,0.002229624],"category_scores_gemma":[0.0006890584,0.0002402964,0.001120373,0.0007665751,0.0002350381,0.001007642,0.0007274277,0.0007808923,0.0008059349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004703686,"about_ca_system_score_gemma":0.0006543807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004388116,"about_ca_topic_score_gemma":0.004914629,"domain_scores_codex":[0.9997905,0.00002471918,0.00001511843,0.00006510613,0.000069895,0.00003469216],"domain_scores_gemma":[0.9998387,0.00003520108,0.00002198701,0.0000198819,0.00006645431,0.00001771332],"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":[0.0003189786,0.0001640804,0.001427277,0.00009990857,0.0001113934,0.0002543296,0.00009841629,0.1172512,0.05405222,0.01240976,0.003690435,0.8101221],"study_design_scores_gemma":[0.000004996756,0.0000672202,0.0004161094,0.000006112527,0.00002346015,0.0001044366,0.00001105549,0.9901806,0.004779304,0.00314533,0.00125273,0.000008664956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01274956,0.0003838098,0.9846917,0.00009231214,0.00004573008,0.00002728133,0.0001007389,0.0008982351,0.001010639],"genre_scores_gemma":[0.6173398,0.0009611554,0.3745774,0.0001945773,0.0001229977,0.00009875811,0.0008236033,0.0001246147,0.005757061],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004388116,"threshold_uncertainty_score":0.008725166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07812892526486764,"score_gpt":0.3442902784390481,"score_spread":0.2661613531741805,"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."}}