{"id":"W2096559184","doi":"10.1109/iembs.2008.4650239","title":"Complimentary artificial neural network approaches for prediction of events in the neonatal intensive care unit","year":2008,"lang":"en","type":"article","venue":"","topic":"Hemodynamic Monitoring and Therapy","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Artificial neural network; Flexibility (engineering); Gradient descent; Computer science; Neonatal intensive care unit; Artificial intelligence; Sensitivity (control systems); Estimation; Mechanical ventilation; Machine learning; Statistics; Mathematics; Engineering; Medicine; Pediatrics","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":[0.002398847,0.000786873,0.000429135,0.0004651486,0.0002012592,0.0007417999,0.0009061201,0.0004880888,0.00198095],"category_scores_gemma":[0.005107198,0.0002766676,0.0004043674,0.0004583546,0.0001920721,0.0008491534,0.0006898373,0.0007710121,0.0003622209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006090872,"about_ca_system_score_gemma":0.0005128801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00530398,"about_ca_topic_score_gemma":0.006524353,"domain_scores_codex":[0.999346,0.0002407919,0.00005171284,0.0001522392,0.0001797708,0.00002950648],"domain_scores_gemma":[0.9987912,0.0006788336,0.0000740824,0.0001420706,0.0002833717,0.00003047977],"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":[0.0006598973,0.0003354359,0.008998419,0.0001428084,0.000110845,0.0001723733,0.0001600681,0.6953338,0.007044179,0.003367409,0.0009372629,0.2827376],"study_design_scores_gemma":[0.00001143664,0.0001098103,0.0009119845,0.00000528325,0.00001208239,0.00001387358,0.000008035354,0.9963175,0.001403832,0.0008857357,0.0003127319,0.000007678511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3170336,0.0006745291,0.6711767,0.0006611019,0.0001704577,0.0003178514,0.0004107626,0.00145942,0.008095522],"genre_scores_gemma":[0.8399442,0.0003797675,0.1558263,0.0001442908,0.00004838163,0.0002399748,0.0001747128,0.0000355355,0.003206919],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00530398,"threshold_uncertainty_score":0.01268649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.124833693933166,"score_gpt":0.2984995828682765,"score_spread":0.1736658889351106,"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."}}