{"id":"W3012331434","doi":"10.2196/17037","title":"A Lightweight Deep Learning Model for Fast Electrocardiographic Beats Classification With a Wearable Cardiac Monitor: Development and Validation Study","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Recurrent neural network; Inference; Wearable computer; Machine learning; Wearable technology; Artificial neural network; Embedded system","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":[0.001360251,0.001019777,0.0007878191,0.0005349533,0.0002177937,0.0004746975,0.001308006,0.0008512188,0.001817722],"category_scores_gemma":[0.002247479,0.0003419337,0.0006666658,0.0004139815,0.0001967397,0.000783841,0.0007126847,0.001200976,0.0006837678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008063359,"about_ca_system_score_gemma":0.001090388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01466706,"about_ca_topic_score_gemma":0.01047212,"domain_scores_codex":[0.9995514,0.00009185965,0.00003189527,0.0001212546,0.0001361363,0.00006739967],"domain_scores_gemma":[0.9990757,0.0002663348,0.00006781688,0.0001240726,0.0003933947,0.00007276342],"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":[0.0008396015,0.001408773,0.01552668,0.0002530991,0.0004013596,0.0002674521,0.00007918185,0.5705529,0.01454111,0.0008888814,0.006275683,0.3889652],"study_design_scores_gemma":[0.00001710449,0.0001434086,0.00105886,0.00000878672,0.00001982941,0.00001785772,0.000006516386,0.9967572,0.001618716,0.0001158808,0.0002295984,0.000006267643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6338676,0.001956062,0.3524259,0.0005665313,0.0003082755,0.0003757823,0.001053694,0.005866472,0.003579621],"genre_scores_gemma":[0.9174519,0.000541726,0.07641856,0.0002106248,0.00003939263,0.0002548441,0.001898874,0.00008034165,0.003103647],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01466706,"threshold_uncertainty_score":0.02916336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02489500214417065,"score_gpt":0.2836765980267737,"score_spread":0.2587815958826031,"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."}}