{"id":"W3033551534","doi":"10.2196/15965","title":"A Predictive Model Based on Machine Learning for the Early Detection of Late-Onset Neonatal Sepsis: Development and Observational Study","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Neonatal and Maternal Infections","field":"Medicine","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Korea Health Industry Development Institute","keywords":"Vital signs; Neonatal intensive care unit; Intensive care; Receiver operating characteristic; Artificial intelligence; Machine learning; Medicine; Feature selection; Sepsis; Intensive care unit; Predictive modelling; Computer science; Feature (linguistics); Neonatal sepsis; Intensive care medicine; Pediatrics; Internal medicine; Surgery","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005696168,0.0008091192,0.000578633,0.001532008,0.0003549877,0.0006772978,0.000903898,0.0005969119,0.001153952],"category_scores_gemma":[0.01674448,0.0002725535,0.0008174762,0.0009411146,0.0002847147,0.001035797,0.0005894641,0.0008596711,0.0002591138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007065116,"about_ca_system_score_gemma":0.001130475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008712166,"about_ca_topic_score_gemma":0.002962515,"domain_scores_codex":[0.9990786,0.0004732777,0.00006678975,0.0001405461,0.0001675422,0.0000732008],"domain_scores_gemma":[0.9886048,0.008675947,0.0005968226,0.0006253747,0.001287276,0.0002098646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001021279,0.001516422,0.8259352,0.00009734233,0.0003428071,0.0006063368,0.0002168285,0.0739076,0.0008555753,0.0008656571,0.001434198,0.09320081],"study_design_scores_gemma":[0.0000458647,0.0006972934,0.06330564,0.00002241496,0.0001227137,0.0002857468,0.0001188814,0.9339035,0.0006971647,0.0004955221,0.0002807208,0.00002451087],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9556302,0.0003899102,0.04239428,0.0001922104,0.00003490178,0.0001745988,0.0004630064,0.0001329123,0.0005881088],"genre_scores_gemma":[0.9865897,0.0002051375,0.01236445,0.00001743508,0.0000209916,0.0001060216,0.0005118058,0.0000110376,0.0001734775],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008712166,"threshold_uncertainty_score":0.03012455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05021631063238528,"score_gpt":0.3026942424112637,"score_spread":0.2524779317788784,"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."}}