{"id":"W2898457169","doi":"10.3390/bios8040101","title":"Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification","year":2018,"lang":"en","type":"article","venue":"Biosensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":167,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Photoplethysmogram; Blood pressure; Medicine; Prehypertension; Artificial intelligence; Cardiology; Internal medicine; Computer science; Filter (signal processing)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001048256,0.0006231866,0.000505901,0.0004546436,0.0001350351,0.000377499,0.0003552906,0.0007003626,0.0007704869],"category_scores_gemma":[0.002306345,0.0001565605,0.000422307,0.0002895203,0.0001818221,0.0006401064,0.0006284626,0.0007478526,0.0002083568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002651082,"about_ca_system_score_gemma":0.0003935808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00292831,"about_ca_topic_score_gemma":0.003033411,"domain_scores_codex":[0.9996132,0.0001324402,0.00002557,0.00008852044,0.00007512166,0.00006517521],"domain_scores_gemma":[0.99942,0.000326871,0.00005421277,0.00004713161,0.000108341,0.00004345024],"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":[0.001221794,0.001098766,0.03293895,0.0002157043,0.0002593824,0.0002348757,0.0001027574,0.09576717,0.03286282,0.0009253527,0.004591313,0.8297811],"study_design_scores_gemma":[0.00005276438,0.0003946478,0.01334506,0.00003059164,0.00008171848,0.0001013976,0.00002197829,0.9742293,0.009358278,0.001416109,0.0009433268,0.00002483122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7210016,0.005476019,0.2653606,0.001809351,0.0002908777,0.0001341368,0.0007121658,0.001559022,0.003656316],"genre_scores_gemma":[0.9599178,0.0006220079,0.03706459,0.0003353053,0.00007813451,0.00003606777,0.0004535417,0.00002026306,0.001472281],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00292831,"threshold_uncertainty_score":0.005822539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008380339420218479,"score_gpt":0.197077723287236,"score_spread":0.1886973838670175,"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."}}