{"id":"W4408740429","doi":"10.1016/j.ins.2025.122114","title":"A multi-modal unsupervised machine learning approach for biomedical signal processing during cardiopulmonary resuscitation","year":2025,"lang":"en","type":"article","venue":"Information Sciences","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cardiopulmonary resuscitation; Modal; Computer science; Signal processing; SIGNAL (programming language); Artificial intelligence; Unsupervised learning; Machine learning; Resuscitation; Medicine; Emergency medicine; Digital signal processing; Computer hardware; Chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008622803,0.0006732704,0.0006337964,0.0008272227,0.0003797917,0.0006066184,0.00101022,0.001090392,0.0009156617],"category_scores_gemma":[0.00201867,0.0002863546,0.001182654,0.0008119027,0.0004947975,0.0007102709,0.0007494347,0.001415996,0.000496082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003947809,"about_ca_system_score_gemma":0.0006740723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002409199,"about_ca_topic_score_gemma":0.003183313,"domain_scores_codex":[0.9994707,0.0001512096,0.00003071353,0.000142688,0.0001607301,0.00004398435],"domain_scores_gemma":[0.9994748,0.0002239098,0.00006558603,0.00004311389,0.0001722263,0.00002049951],"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":[0.0001652138,0.0001936063,0.001493641,0.0002493952,0.0002102828,0.0002107538,0.00023974,0.4373636,0.03383274,0.01051689,0.003561847,0.5119624],"study_design_scores_gemma":[0.000002671567,0.00003645199,0.0003528245,0.000007790941,0.00001417257,0.00003668789,0.00001082241,0.9938748,0.002231971,0.002378237,0.001042342,0.00001120829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004292952,0.0002815205,0.9945573,0.000101579,0.00003213059,0.00002481846,0.00003391848,0.0002058158,0.0004699642],"genre_scores_gemma":[0.3245189,0.001203893,0.6668508,0.0003504205,0.0003694447,0.0003237825,0.0005208256,0.0001672295,0.005694547],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002409199,"threshold_uncertainty_score":0.004790366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01969258047446174,"score_gpt":0.2620998308391052,"score_spread":0.2424072503646434,"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."}}