Predicting the severity of dengue fever among children admitted to Angkor hospital from clinical features and laboratory indicators : application of classification tree analysis
Issued Date
2014
Copyright Date
2014
Resource Type
Language
eng
File Type
application/pdf
No. of Pages/File Size
xi, 140 leaves : ill.
Access Rights
open access
Rights
ผลงานนี้เป็นลิขสิทธิ์ของมหาวิทยาลัยมหิดล ขอสงวนไว้สำหรับเพื่อการศึกษาเท่านั้น ต้องอ้างอิงแหล่งที่มา ห้ามดัดแปลงเนื้อหา และห้ามนำไปใช้เพื่อการค้า
Rights Holder(s)
Mahidol University
Bibliographic Citation
Thematic Paper (M.Sc. (Biomedical and Health Informatics))--Mahidol University, 2014
Suggested Citation
Khansoudaphone Phakhounthong Predicting the severity of dengue fever among children admitted to Angkor hospital from clinical features and laboratory indicators : application of classification tree analysis . Thematic Paper (M.Sc. (Biomedical and Health Informatics))--Mahidol University, 2014. Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/95232
Title
Predicting the severity of dengue fever among children admitted to Angkor hospital from clinical features and laboratory indicators : application of classification tree analysis
Author(s)
Advisor(s)
Abstract
Background: Dengue fever (DF) / Dengue Haemorrhagic Fever (DHF) is a viral and re-emerging disease, commonly occurring in tropical and subtropical areas. Furthermore, clinical features and laboratory abnormalities of dengue infection are also similar to other febrile illnesses. The goal of this study is to develop predictive models to characterize dengue and its severity by early clinical and laboratory measures using statistical and data mining tools. Methods and findings: We performed a retrospective study and retrieved data from a study of febrile illness in children at Angkor Hospital for Children in Cambodia. A total of 1225 febrile episodes were in our analysis, 198 were confirmed dengue patients. Classification and regression tree (CART) and logistic regression analysis were used independently to differentiate: dengue versus non-dengue (model 1), severe dengue versus non-severe dengue (model 2) and death versus survival (model 3). Highlighting result of model 2, classifying severe dengue, the decision tree algorithm using pulse rate, hematocrit, Glasgow coma score, urine protein, creatinine and platelet count has outperformed the logistic regression model with sensitivity and specificity of 71% and 68.7%, respectively. Conclusions: Our decision tree algorithm using simple clinical and laboratory indicators has a high classification accuracy in predicting pediatric patients who develop severe dengue. This model is potentially useful for guiding a patient monitoring plan and outpatient management of fever.
Description
Biomedical and Health Informatics (Mahidol University 2014)
Degree Name
Master of Science
Degree Level
Master's degree
Degree Department
Faculty of Tropical Medicine
Degree Discipline
Biomedical and Health Informatics
Degree Grantor(s)
Mahidol University