{"id":"W4306910162","doi":"10.1109/jbhi.2022.3215995","title":"Noisy Neonatal Chest Sound Separation for High-Quality Heart and Lung Sounds","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Phonocardiography and Auscultation Techniques","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Children's Hospital; University of British Columbia","funders":"","keywords":"Non-negative matrix factorization; Heart sounds; Computer science; Stethoscope; Speech recognition; Respiratory sounds; Noise (video); Sound quality; Auscultation; Pattern recognition (psychology); Artificial intelligence; SIGNAL (programming language); Medicine; Matrix decomposition; Cardiology; Asthma; Radiology; Internal 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":[0.001252853,0.0008963674,0.0005469864,0.0009370396,0.000304254,0.0006590811,0.0004968221,0.0008513137,0.001019797],"category_scores_gemma":[0.005216091,0.0001816532,0.0007430741,0.000547928,0.0003623428,0.000783932,0.000644342,0.0009178472,0.0007329768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002649142,"about_ca_system_score_gemma":0.0006784591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001790304,"about_ca_topic_score_gemma":0.002955939,"domain_scores_codex":[0.9991952,0.0001997901,0.00006033395,0.0001693468,0.0003191967,0.00005609601],"domain_scores_gemma":[0.9984275,0.0007389539,0.000148168,0.0001377902,0.0004859308,0.00006159925],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007527434,0.0001632781,0.006559822,0.0005071922,0.0001307707,0.0003842923,0.0002145689,0.05079979,0.211207,0.002078253,0.003063525,0.7241388],"study_design_scores_gemma":[0.0000632627,0.0003868768,0.02354742,0.0001194479,0.0001451199,0.001366514,0.0001813357,0.8103911,0.1502614,0.002926151,0.0105135,0.00009778723],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05142403,0.0007727196,0.9456469,0.0001951433,0.0001264796,0.00005129501,0.0001548711,0.0006050797,0.00102348],"genre_scores_gemma":[0.3838672,0.001063244,0.6111663,0.0001888083,0.0001732182,0.0001331749,0.001035027,0.0001985223,0.002174576],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001790304,"threshold_uncertainty_score":0.006625831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04363179540909044,"score_gpt":0.3937606683256403,"score_spread":0.3501288729165499,"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."}}