Detecting Dengue Fever in Children: Using Sequencing Symptom Patterns for An Online Assessment Approach

摘要

cqvip:Background: Dengue fever (DF) is an important health problem in Asia. We examined it using its clinical symptoms to predict DF. Methods: We extracted statistically significant features from 17 DF-related clinical symptoms in 177 pediatric patients (69 diagnosed with DF) using the unweighted summation score and the non-parametric HT person fit statistic, which jointly combine the weighted score (yielded by logistic regression) to predict DF risk. Results: Six symptoms (Family History, Fever 39C, Skin Rash, Petechiae, Abdominal Pain, and Weakness) significantly predicted DF. When a cutoff point of 1.03 (p = 0.26) suggested combining the weighted score and the HT coefficient, the sensitivity was 0.91 and the specificity was 0.76. The area under the ROC curve was 0.88, which was a better predictor: specificity was 5.56% higher than for the traditional logistic regression. Conclusions: Six simple symptoms analyzed using logistic regression were useful and valid for early detection of DF risk in children. A better predictive specificity increased after combining the non-parametric HT coefficient to the weighted regression score. A self-assessment using patient smart phones is available to discriminate DF and may eliminate the need for a costly and time-consuming dengue laboratory test.