{"id":"W3170604628","doi":"10.1016/j.cmpb.2021.106222","title":"Classification of blood pressure in critically ill patients using photoplethysmography and machine learning","year":2021,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Photoplethysmogram; Critically ill; Blood pressure; Computer science; Intensive care medicine; Artificial intelligence; Medicine; Machine learning; Internal medicine; Computer vision","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004552845,0.0001331596,0.0002816841,0.0002261614,0.00002172273,0.00002187579,0.00005573227,0.00008401326,0.000002112375],"category_scores_gemma":[0.000119801,0.0001271208,0.00002291413,0.0006007556,0.0001061045,0.00006680249,0.0000729567,0.0002203796,3.363486e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006852938,"about_ca_system_score_gemma":0.000006473784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003685831,"about_ca_topic_score_gemma":0.000004945868,"domain_scores_codex":[0.998974,0.0001828418,0.0003182672,0.0002276332,0.0001098236,0.0001874284],"domain_scores_gemma":[0.9994335,0.0001802954,0.00003617242,0.0001056138,0.0001776157,0.00006681055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000005448027,0.00009779588,0.4574694,0.0004324856,0.00004607024,0.00001330091,0.0003878129,0.00004355585,0.1465955,0.00003268122,5.528319e-7,0.3948754],"study_design_scores_gemma":[0.007067156,0.001227538,0.6757845,0.002890043,0.0004568692,0.00007295991,0.0005290121,0.2291942,0.07665337,0.002247809,0.002921212,0.0009553488],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8484753,0.009552034,0.1413227,0.00003704836,0.0003087671,0.000226012,0.000002634825,0.00004674385,0.00002873568],"genre_scores_gemma":[0.6161172,0.0001952184,0.3835791,0.00001949721,0.00005290148,0.000007365678,0.00001420688,0.00001380514,7.009531e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3939201,"threshold_uncertainty_score":0.5183837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04002412902654815,"score_gpt":0.3080432887451277,"score_spread":0.2680191597185795,"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."}}