{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009196308,0.00007202961,0.0001421137,0.0000330907,0.00006375698,0.000001070376,0.00005662919,0.00004042467,0.000005805833],"category_scores_gemma":[0.00001233355,0.0000477324,0.00005712416,0.0000987832,0.00004584158,0.00002458424,0.00001006137,0.0001039916,8.142602e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002368537,"about_ca_system_score_gemma":0.00002540127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001121846,"about_ca_topic_score_gemma":0.00002136214,"domain_scores_codex":[0.9994155,0.00003558941,0.0001750213,0.0001080845,0.0001311686,0.000134679],"domain_scores_gemma":[0.9996409,0.00006701684,0.00003343525,0.0001314026,0.0001022451,0.00002496425],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001713966,0.0001738423,0.9331795,0.0001385725,0.0001621243,0.00002606079,0.02299897,0.004049161,0.0003503817,0.0008829723,0.0009205611,0.03540388],"study_design_scores_gemma":[0.003789005,0.001457307,0.8507649,0.0001605341,0.00009954723,0.0002421628,0.06051059,0.07926771,0.001780878,0.0007410181,0.001021159,0.0001651333],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973946,0.0001561644,0.00099384,0.0003952287,0.0002642773,0.0005390523,0.00002368304,0.00001869084,0.0002144462],"genre_scores_gemma":[0.9987616,0.000008198299,0.0002883439,0.0002486728,0.0003833249,0.00003370279,0.0002098161,0.000008330667,0.00005799508],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08241455,"threshold_uncertainty_score":0.1946471,"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."}}