{"id":"W4408958860","doi":"10.1016/j.imu.2025.101635","title":"Investigating the accuracy of neural networks for blood pressure prediction in the ICU","year":2025,"lang":"en","type":"article","venue":"Informatics in Medicine Unlocked","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Queen's University; Queen's University Belfast; Health Foundation","keywords":"Blood pressure; Artificial neural network; Computer science; Artificial intelligence; Data mining; Intensive care medicine; Medicine; Internal medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005810286,0.0009981614,0.000536439,0.0010879,0.0003440347,0.001167137,0.0007799745,0.001313286,0.00107237],"category_scores_gemma":[0.03931471,0.0002948143,0.0003763529,0.0007712509,0.0004155162,0.001500203,0.000608111,0.001190573,0.000423726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008988248,"about_ca_system_score_gemma":0.0007641402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01891636,"about_ca_topic_score_gemma":0.01096705,"domain_scores_codex":[0.9979272,0.0008791843,0.0001815795,0.0002955666,0.0005125675,0.0002038452],"domain_scores_gemma":[0.9778587,0.01742397,0.0008548712,0.001014584,0.002571513,0.0002763236],"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":[0.001792175,0.0004870092,0.1642838,0.0002273844,0.0004389732,0.0001878945,0.0002712556,0.6546078,0.004738438,0.001063419,0.002122729,0.1697792],"study_design_scores_gemma":[0.00000964117,0.0002482257,0.01443641,0.00003265086,0.00003596125,0.0000303739,0.00008685175,0.9812673,0.003155784,0.0004496124,0.0002335496,0.00001367016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.967335,0.001530274,0.02414322,0.001052784,0.0001508978,0.00004488338,0.0004465182,0.0003870144,0.004909392],"genre_scores_gemma":[0.9928269,0.0002608227,0.005489267,0.00007350909,0.00003485538,0.00001129376,0.0004273804,0.00002642242,0.0008494199],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01891636,"threshold_uncertainty_score":0.0376125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01587820325276292,"score_gpt":0.2616381573279348,"score_spread":0.2457599540751719,"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."}}