{"id":"W4323027496","doi":"10.22489/cinc.2022.110","title":"Cuff-less Estimation of Blood Pressure from Vibrational Cardiography Using a Convolutional Neural Network","year":2022,"lang":"en","type":"article","venue":"Computing in cardiology","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Convolutional neural network; Computer science; Blood pressure; Estimation; Artificial neural network; Artificial intelligence; Medicine; Internal medicine; Engineering","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":[0.0004297848,0.0006075383,0.0003527337,0.0004133049,0.0001763013,0.0003462152,0.0005447457,0.0005021292,0.0008660883],"category_scores_gemma":[0.0010478,0.0002543937,0.0003695463,0.000323156,0.000163733,0.0004025986,0.0003778446,0.0005867883,0.0003213685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004789768,"about_ca_system_score_gemma":0.0006152162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01226937,"about_ca_topic_score_gemma":0.01201507,"domain_scores_codex":[0.999845,0.00002377355,0.000008966324,0.00005289343,0.00003759537,0.00003182763],"domain_scores_gemma":[0.9997552,0.00010723,0.00002524558,0.00002290255,0.00007791648,0.00001150565],"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":[0.0004333949,0.0003816422,0.009479155,0.0001029016,0.0002079706,0.000212393,0.00008141175,0.3113454,0.05605134,0.001279352,0.002459968,0.6179651],"study_design_scores_gemma":[0.000003330802,0.000039408,0.001858446,0.000005200291,0.00001704359,0.00002409209,0.000003642877,0.9934468,0.004238568,0.0001545961,0.0002036696,0.000005218782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2707244,0.0009276515,0.7228831,0.0002740777,0.0001519974,0.00006871561,0.0002845658,0.001935466,0.002750018],"genre_scores_gemma":[0.9078304,0.0003807661,0.08680084,0.00009935046,0.00004755853,0.00006446708,0.0004990786,0.00002769888,0.004249878],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01226937,"threshold_uncertainty_score":0.02439594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01647004377961946,"score_gpt":0.2249555417311325,"score_spread":0.2084854979515131,"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."}}