{"id":"W2121071670","doi":"10.1109/tim.2011.2123210","title":"Feature-Based Neural Network Approach for Oscillometric Blood Pressure Estimation","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Heart Rate Variability and Autonomic Control","field":"Medicine","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Backpropagation; Artificial neural network; Waveform; Computer science; Levenberg–Marquardt algorithm; Feature (linguistics); Mean squared error; Artificial intelligence; Pattern recognition (psychology); Algorithm; Approximation error; Mathematics; Statistics","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":[0.0005783105,0.0005655438,0.0006159871,0.0005708073,0.0001851228,0.0003858789,0.0007208391,0.000657559,0.0009928519],"category_scores_gemma":[0.001886164,0.0002152045,0.0003190007,0.0006845183,0.0001916028,0.0006065547,0.000307933,0.0005585949,0.0003238586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002989512,"about_ca_system_score_gemma":0.0002909021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002076507,"about_ca_topic_score_gemma":0.001998938,"domain_scores_codex":[0.9996952,0.00007070015,0.00002651908,0.00007720596,0.0001094411,0.0000209318],"domain_scores_gemma":[0.9995912,0.0002077428,0.0000423182,0.00003384165,0.0001176123,0.000007298208],"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":[0.0001864008,0.0001372185,0.00215294,0.0002073216,0.0001474579,0.0001403547,0.00007124092,0.3014903,0.02318187,0.003677265,0.001095513,0.6675122],"study_design_scores_gemma":[0.000007968893,0.00004356256,0.0009143324,0.00001010202,0.00002047547,0.0000655867,0.00000470858,0.9941773,0.002770917,0.001174761,0.0007980526,0.00001219958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008335995,0.0002985942,0.9905668,0.00003168119,0.00002666816,0.00001739114,0.0000349667,0.0002759236,0.0004120376],"genre_scores_gemma":[0.4491428,0.0006487913,0.5475799,0.000105996,0.0001155195,0.0002390727,0.0002365255,0.00005205754,0.001879247],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002076507,"threshold_uncertainty_score":0.004128814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06278541149139484,"score_gpt":0.2563310898294172,"score_spread":0.1935456783380224,"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."}}