{"title":"临床医生对疑似晚发型脓毒症的早产儿治疗的预测-一种ML方法","authors":"Yifei Hu, Vincent C. S. Lee, K. Tan","doi":"10.1109/ICIEA.2018.8397888","DOIUrl":null,"url":null,"abstract":"As a prevalent disease of preterm infants, late-onset neonatal sepsis has taken up a huge proportion of morbidity and mortality of newborn babies. We have been continuously capturing vital signs of preterm infants in NICU, and proposed a non-invasive method based on machine learning techniques to predict the clinicians' treatment on them. Then we provide evaluation of predictive models and prove their feasibility. Our models could help the pediatricians make wiser clinical decision, such as more accurate treatment, avoiding the abuse of antibiotics to some extent.","PeriodicalId":140420,"journal":{"name":"2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Prediction of clinicians' treatment in preterm infants with suspected late-onset sepsis — An ML approach\",\"authors\":\"Yifei Hu, Vincent C. S. Lee, K. Tan\",\"doi\":\"10.1109/ICIEA.2018.8397888\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As a prevalent disease of preterm infants, late-onset neonatal sepsis has taken up a huge proportion of morbidity and mortality of newborn babies. We have been continuously capturing vital signs of preterm infants in NICU, and proposed a non-invasive method based on machine learning techniques to predict the clinicians' treatment on them. Then we provide evaluation of predictive models and prove their feasibility. Our models could help the pediatricians make wiser clinical decision, such as more accurate treatment, avoiding the abuse of antibiotics to some extent.\",\"PeriodicalId\":140420,\"journal\":{\"name\":\"2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)\",\"volume\":\"50 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIEA.2018.8397888\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIEA.2018.8397888","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Prediction of clinicians' treatment in preterm infants with suspected late-onset sepsis — An ML approach
As a prevalent disease of preterm infants, late-onset neonatal sepsis has taken up a huge proportion of morbidity and mortality of newborn babies. We have been continuously capturing vital signs of preterm infants in NICU, and proposed a non-invasive method based on machine learning techniques to predict the clinicians' treatment on them. Then we provide evaluation of predictive models and prove their feasibility. Our models could help the pediatricians make wiser clinical decision, such as more accurate treatment, avoiding the abuse of antibiotics to some extent.