{"id":"W4390573466","doi":"10.48550/arxiv.2401.01013","title":"A Novel Transformer-Based Self-Supervised Learning Method to Enhance Photoplethysmogram Signal Artifact Detection","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de Recherche du Québec - Santé; Institut de Valorisation des Données; Université de Montréal","keywords":"Computer science; Robustness (evolution); Artificial intelligence; Transformer; Machine learning; Boosting (machine learning); Semi-supervised learning; Labeled data; Pattern recognition (psychology); Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.001384362,0.0006495688,0.0007284304,0.0007642378,0.0002746421,0.0005165518,0.001423956,0.0007257333,0.001336455],"category_scores_gemma":[0.002913608,0.0002853397,0.0007395821,0.0005676821,0.0005814415,0.00105563,0.001207453,0.0009228737,0.0005965812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004649978,"about_ca_system_score_gemma":0.0009035867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001300086,"about_ca_topic_score_gemma":0.002482842,"domain_scores_codex":[0.9994724,0.0001509249,0.00002851453,0.0001573164,0.0001488728,0.0000420114],"domain_scores_gemma":[0.9989139,0.0004048603,0.0001068838,0.0001566985,0.0003487087,0.00006898124],"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":[0.0004547305,0.0003649462,0.003221083,0.0001789596,0.0001236414,0.0001478709,0.0001663648,0.1480451,0.04754389,0.007352236,0.006072463,0.7863288],"study_design_scores_gemma":[0.00001292635,0.00005277341,0.0002478208,0.00000342302,0.00001122431,0.00004498045,0.000005367135,0.991734,0.005788849,0.001474957,0.0006167557,0.000006866222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02072719,0.0001596348,0.9770479,0.0001531026,0.00004100605,0.0000479858,0.00006780177,0.0009996269,0.0007557226],"genre_scores_gemma":[0.4734027,0.0001992143,0.5194652,0.0004703907,0.0001194306,0.000149938,0.000570591,0.0002686597,0.005353872],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001423956,"threshold_uncertainty_score":0.007321298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05945955498789388,"score_gpt":0.2694447775835405,"score_spread":0.2099852225956466,"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."}}