{"id":"W4292394411","doi":"10.3390/bioengineering9080402","title":"Subject-Based Model for Reconstructing Arterial Blood Pressure from Photoplethysmogram","year":2022,"lang":"en","type":"article","venue":"Bioengineering","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Division of Graduate Education; Guangxi Innovation-Driven Development Project; Natural Sciences and Engineering Research Council of Canada; Guilin University of Electronic Technology; National Natural Science Foundation of China; Canada Research Chairs","keywords":"Photoplethysmogram; Similarity (geometry); Blood pressure; Mean squared error; Mean absolute error; Artificial intelligence; Pattern recognition (psychology); Computer science; SIGNAL (programming language); Mean arterial pressure; Cardiology; Mathematics; Speech recognition; Medicine; Internal medicine; Heart rate; Statistics; Computer vision; Filter (signal processing)","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":[0.0003372488,0.0004913271,0.00047408,0.0002800806,0.0001903081,0.0004163825,0.0006854776,0.000727305,0.002063552],"category_scores_gemma":[0.0005060331,0.0002642862,0.0006508134,0.0002516569,0.0002107693,0.0003496954,0.0003734348,0.0008135495,0.0005624691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003625043,"about_ca_system_score_gemma":0.0006600686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01381958,"about_ca_topic_score_gemma":0.01103306,"domain_scores_codex":[0.9998922,0.00002016414,0.000006323201,0.00004480045,0.00001930916,0.00001718493],"domain_scores_gemma":[0.9998997,0.00003704928,0.00001122373,0.000008337706,0.00003755292,0.000006066626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002160068,0.0001005331,0.0032253,0.00007177857,0.0001190674,0.0001683818,0.00006595937,0.8806313,0.008188339,0.002028683,0.001155136,0.1040295],"study_design_scores_gemma":[0.000003716441,0.00002317081,0.000527908,0.000003319957,0.00001286176,0.00002091078,0.000002412378,0.9983076,0.0005963787,0.000286416,0.0002114709,0.00000378864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1321352,0.001059949,0.8597033,0.0003969045,0.0002113927,0.0001068214,0.0006595216,0.001389482,0.00433749],"genre_scores_gemma":[0.9298258,0.0006016614,0.0563983,0.0001644758,0.00007463938,0.0002660779,0.0008373313,0.00006122408,0.0117704],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01381958,"threshold_uncertainty_score":0.02747828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01426117740197951,"score_gpt":0.2013375151101653,"score_spread":0.1870763377081858,"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."}}