{"id":"W7117711065","doi":"10.18280/isi.301124","title":"Cuffless Blood Pressure Estimation Using AI Models: A Comparative Study of SVR, LSTM, and LightGBM","year":2025,"lang":"","type":"article","venue":"Ingénierie des systèmes d information","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Blood pressure; Pattern recognition (psychology); Sphygmomanometer; Measure (data warehouse); Noise (video)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000466456,0.0005722897,0.0008730787,0.0009155638,0.0004446256,0.000512281,0.0003003325,0.0003036465,0.000008306583],"category_scores_gemma":[0.0001398614,0.0006504486,0.00009371511,0.001171194,0.0001905919,0.007424043,0.0002527937,0.0004249519,0.000005796006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002625055,"about_ca_system_score_gemma":0.0001686627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004866446,"about_ca_topic_score_gemma":0.00002554183,"domain_scores_codex":[0.9968269,0.0001699988,0.001706066,0.0003027369,0.0005084935,0.0004857817],"domain_scores_gemma":[0.9978682,0.0002013015,0.000607662,0.0004698776,0.0007300214,0.0001229309],"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":[0.000122384,0.000322659,0.004486961,0.005912815,0.001559214,0.000003479707,0.08622307,0.8843802,0.00588861,0.002923387,0.00003098302,0.008146303],"study_design_scores_gemma":[0.002388909,0.0004252427,0.001770693,0.002995563,0.00153526,0.00001804064,0.01789602,0.9361148,0.03137904,0.004829623,0.0000322933,0.0006144494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7815111,0.002888415,0.2099594,0.000006721863,0.0007317619,0.001758905,0.00005313107,0.0001562497,0.002934321],"genre_scores_gemma":[0.9955944,0.00008210281,0.004068491,0.00001371004,0.00006761879,0.00009802758,0.00002515116,0.00003061997,0.00001990691],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2140832,"threshold_uncertainty_score":0.9995947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02632581453033333,"score_gpt":0.2708600273976933,"score_spread":0.2445342128673599,"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."}}