{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001361134,0.0002927158,0.0002710978,0.0001584579,0.0001940616,0.00007100937,0.0002905694,0.00007877027,0.000066542],"category_scores_gemma":[0.0000405473,0.0003792006,0.0001501867,0.0002182602,0.00001695795,0.0001292068,0.00007939085,0.0002898369,0.000002584404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007314592,"about_ca_system_score_gemma":0.0000341727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003024312,"about_ca_topic_score_gemma":0.000005483294,"domain_scores_codex":[0.9986239,0.00001527101,0.0003028492,0.00033872,0.0002201414,0.000499117],"domain_scores_gemma":[0.9993496,0.0001540634,0.00004403532,0.0003163106,0.00002791387,0.0001080773],"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":[0.00001400549,0.00001373865,0.0002888499,0.00006585792,0.0001019768,0.000004323542,0.00009951639,0.5575334,0.4412778,0.00002043989,0.00006845931,0.0005115459],"study_design_scores_gemma":[0.0009404004,0.00004266232,0.00002790215,0.00002623032,0.0001259797,0.000006080516,0.00006356905,0.8047028,0.1923733,0.0000902585,0.00118885,0.0004118958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6987525,0.001039441,0.2935946,0.00001448119,0.003544541,0.0006795233,0.0007467966,0.001490749,0.0001374159],"genre_scores_gemma":[0.9666693,0.000003558713,0.0319064,0.00001268734,0.0006053925,0.0005629467,0.00007741174,0.00014061,0.0000217074],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2679169,"threshold_uncertainty_score":0.999866,"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."}}