Journal of pediatric hematology/oncology · 2024
Machine Learning-based Prediction of Blood Stream Infection in Pediatric Febrile Neutropenia.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Park Jun Sung, Song Jongkeon, Yoo Reenar, Kim Dahyun, Chun Min Kyo, Han Jeeho, Lee Jeong-Yong, Choi Seung Jun, Lee Jong Seung, Ryu Jeong-Min, Kang Sung Han, Koh Kyung-Nam, Im Ho Joon, Kim Hyery
Аннотация
OBJECTIVES: This study aimed to develop machine learning (ML) prediction models for identifying bloodstream infection (BSI) and septic shock (SS) in pediatric patients with cancer who presenting febrile neutropenia (FN) at emergency department (ED) visit.
MATERIALS AND METHODS: A retrospective study was conducted on patients, younger than 18 years of age, who visited a tertiary university-affiliated hospital ED due to FN between January 2004 and August 2022. ML models, based on XGBoost, were developed for BSI and SS prediction.
RESULTS: After applying the exclusion criteria, we identified 4423 FN events during the study period. We identified 195 (4.4%) BSI and 107 (2.4%) SS events. The BSI and SS models demonstrated promising performance, with area under the receiver operating characteristic curve values of 0.87 and 0.88, respectively, which were superior to those of the logistic regression models. Clinical features, including body temperature, some laboratory results, vital signs, and diagnosis of acute myeloblastic leukemia were identified as significant predictors.
CONCLUSIONS: The ML-based prediction models, which use data obtainable at ED visits may be valuable tools for ED physicians to predict BSI or SS.
MATERIALS AND METHODS: A retrospective study was conducted on patients, younger than 18 years of age, who visited a tertiary university-affiliated hospital ED due to FN between January 2004 and August 2022. ML models, based on XGBoost, were developed for BSI and SS prediction.
RESULTS: After applying the exclusion criteria, we identified 4423 FN events during the study period. We identified 195 (4.4%) BSI and 107 (2.4%) SS events. The BSI and SS models demonstrated promising performance, with area under the receiver operating characteristic curve values of 0.87 and 0.88, respectively, which were superior to those of the logistic regression models. Clinical features, including body temperature, some laboratory results, vital signs, and diagnosis of acute myeloblastic leukemia were identified as significant predictors.
CONCLUSIONS: The ML-based prediction models, which use data obtainable at ED visits may be valuable tools for ED physicians to predict BSI or SS.