{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002226236,0.0001023099,0.0001696228,0.00004718098,0.0001321243,0.00001242133,0.00006164204,0.00006512312,0.00001899096],"category_scores_gemma":[0.000236181,0.0000645887,0.00004206015,0.0001163721,0.00005795119,0.00009726255,0.0000476602,0.0003029023,0.000003926544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002581412,"about_ca_system_score_gemma":0.0001508482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001474659,"about_ca_topic_score_gemma":0.000008970482,"domain_scores_codex":[0.9988966,0.00001555914,0.0003910579,0.0000717509,0.0005150269,0.0001100243],"domain_scores_gemma":[0.9993364,0.0002187914,0.0001142316,0.00006803335,0.00009917998,0.0001634108],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01419543,0.00303904,0.2711,0.004887484,0.002044134,0.00005237901,0.2109638,0.07823869,0.0005038504,0.0004049486,0.001035903,0.4135343],"study_design_scores_gemma":[0.001911875,0.001361341,0.03534608,0.00008413173,0.0000549649,0.000007319676,0.0009534584,0.9582909,0.000542327,0.00001940901,0.001364443,0.00006379715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8850512,0.00001509743,0.1130084,0.0009053514,0.00003603576,0.0008516583,0.00002628354,0.00004800463,0.00005795609],"genre_scores_gemma":[0.9965902,0.000004740597,0.00143172,0.00162583,0.00003715092,0.0002090097,0.00003987799,0.000008619339,0.00005288016],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8800521,"threshold_uncertainty_score":0.2633851,"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."}}