Abstract
Dengue virus infection is a major global health problem. While dengue fever rarely results in serious complications, the more severe illness dengue hemorrhagic fever (DHF) has a significant mortality rate due to the associated plasma leakage. Proper care thus requires identifying patients with DHF among those with suspected dengue so that they can be provided with adequate and prompt fluid replacement. In this paper, we use 18 years of pediatric patient data collected prospectively from two hospitals in Thailand to develop models to predict DHF among patients with suspected dengue. The best model using pooled data from both hospitals achieved an AUC of 0.92. We then investigate the generalizability of the models by constructing a model for one hospital and testing it on the other, a question that has not yet been adequately explored in the literature on DHF prediction. For some models, we find significant degradation in performance. We show this is due to differences in attribute values among the two hospital patient populations. Possible sources of this are differences in the definition of attributes and differences in the pathogenesis of the disease among the two sub-populations. We conclude that while high predictive accuracy is possible, care must be taken when seeking to apply DHF predictive models from one clinical setting to another.
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References
Barak-Corren, Y., Chaudhari, P., Perniciaro, J., Waltzman, M., Fine, A.M., Reis, B.Y.: Prediction across healthcare settings: a case study in predicting emergency department disposition. NPJ Digit. Med. 4(1), 1–7 (2021)
Bhatt, S., et al.: The global distribution and burden of dengue. Nature 496(7446), 504–507 (2013)
Burns, M.L., Kheterpal, S.: Machine learning comes of age: local impact versus national generalizability (2020)
Carrasco, L.R., et al.: Predictive tools for severe dengue conforming to world health organization 2009 criteria. PLoS Negl. Trop. Dis. 8(7), e2972 (2014)
Chandna, A., et al.: Prediction of disease severity in young children presenting with acute febrile illness in resource-limited settings: a protocol for a prospective observational study. BMJ Open 11(1), e045826 (2021)
Fernández, E., Smieja, M., Walter, S.D., Loeb, M.: A retrospective cohort study to predict severe dengue in Honduran patients. BMC Infect. Dis. 17(1), 1–6 (2017)
Gomes, A.L.V., et al.: Classification of dengue fever patients based on gene expression data using support vector machines. PLoS ONE 5(6), e11267 (2010)
Grzymala-Busse, J.W.: Discretization based on entropy and multiple scanning. Entropy 15(5), 1486–1502 (2013)
Herath, H., et al.: Prediction of plasma leakage phase of dengue in resource limited settings. Clin. Epidemiol. Global Health 7(3), 279–282 (2019)
Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems 30 (2017)
Lee, V.J., Lye, D., Sun, Y., Leo, Y.: Decision tree algorithm in deciding hospitalization for adult patients with dengue Haemorrhagic fever in Singapore. Trop. Med. Int. Health 14(9), 1154–1159 (2009)
McDermott, M.B., Wang, S., Marinsek, N., Ranganath, R., Foschini, L., Ghassemi, M.: Reproducibility in machine learning for health research: Still a ways to go. Sci. Transl. Med. 13(586), eabb1655 (2021)
Phakhounthong, K., et al.: Predicting the severity of dengue fever in children on admission based on clinical features and laboratory indicators: application of classification tree analysis. BMC Pediatr. 18(1), 1–9 (2018)
Tan, K.W.: Dynamic dengue Haemorrhagic fever calculators as clinical decision support tools in adult dengue. Trans. R. Soc. Trop. Med. Hyg. 114(1), 7–15 (2020)
World Health Organization and Special Programme for Research and Training in Tropical Diseases and World Health Organization. Department of Control of Neglected Tropical Diseases and World Health Organization. Epidemic and Pandemic Alert: Dengue: guidelines for diagnosis, treatment, prevention and control. World Health Organization (2009)
Yang, J., Soltan, A.A., Clifton, D.A.: Machine learning generalizability across healthcare settings: insights from multi-site COVID-19 screening. NPJ Digit. Med. 5(1), 1–8 (2022)
Acknowledgment
This work was partially supported by National Science and Technology Development Agency grant no. P-20-52599, Faculty of Medicine Siriraj Hospital, Mahidol University grant no. R016536004, a grant from the Mahidol University Office of International Relations to Haddawy in support of the Mahidol-Bremen Medical Informatics Research Unit, a Study Group grant from the Hanse-Wissenschaftskolleg Institute for Advanced Study to Haddawy, a fellowship from the Hanse-Wissenschaftskolleg Institute for Advanced Study, and by a Young Researcher grant from Mahidol University to Su Yin.
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Haddawy, P. et al. (2023). Prognostic Prediction of Pediatric DHF in Two Hospitals in Thailand. In: Juarez, J.M., Marcos, M., Stiglic, G., Tucker, A. (eds) Artificial Intelligence in Medicine. AIME 2023. Lecture Notes in Computer Science(), vol 13897. Springer, Cham. https://doi.org/10.1007/978-3-031-34344-5_36
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DOI: https://doi.org/10.1007/978-3-031-34344-5_36
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