Quality of Symptom-Based Diagnosis of Rotavirus Infection Based on Mathematical Modeling
Rotavirus is the leading cause of severe childhood gastroenteritis worldwide. The laboratory diagnosis requires testing of fecal specimens with commercial assays that often are not available in low resource settings. Therefore, estimation of rotavirus presence based on clinical symptoms is expected to improve the disease management without laboratory verification.
We aimed to develop and compare different mathematical approaches to model-based evaluation of expected rotavirus presence in patients with similar clinical symptoms. Two clinical datasets were used to develop clinical evaluation models of rotavirus presence or absence based on Bayesian network (BN), linear and nonlinear regression.
The developed models produced different levels of reliability. BN compared with regression models showed better rotavirus detection results according to optimal cut-off points. Such approach is viable to help physicians refer patient to the group with suspected rotavirus infection to avoid unnecessary antibiotic treatment and to prevent rotavirus infection spread in a hospital ward.
KeywordsRotavirus infection Symptoms Bayesian network Regression
The authors thank the Indonesia Endowment Fund for Education (LPDP) for funding Ph.D. fellowship to Mohamad S. Hakim.
Conflict of Interest
The authors declare to have no conflict of interest.
- 1.GBD 2015 Mortality and Causes of Death Collaborators: Global, regional and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 385(9963), 117–171 (2015)Google Scholar
- 5.Pratiwi, E., Setiawaty, V., Putranto, R.H.: Molecular characteristics of rotavirus isolated from a diarrhea outbreak in October 2008 in Bintuni Bay, Papua, Indonesia. Virology (Auckl) 5, 11–14 (2014)Google Scholar
- 6.Corwin, A.L., et al.: A large outbreak of probable rotavirus in Nusa Tenggara Timur Indonesia. Am. J. Trop. Med. Hyg. 72(4), 488–494 (2005)Google Scholar
- 11.Oliveira, J.: A shotgun wedding: business decision support meets clinical decision support. J. Healthc. Inf. Manag. 16(4), 28–33 (2002)Google Scholar
- 12.DeGruy, K.B.: Healthcare applications of knowledge discovery in databases. J. Healthc. Inf. Manag. 14(2), 59–69 (2000)Google Scholar
- 13.Hardin, J.M., Chhieng, D.C.: Data mining and clinical decision support systems. In: Clinical Decision Support Systems, pp. 44–63. Springer (2007)Google Scholar
- 14.Perreault, L.E., Metzger, J.B.: A pragmatic framework for understanding clinical decision support. J. Healthc. Inf. Manage. 13, 5–22 (1999)Google Scholar
- 22.Dai, M., et al.: Mutation of the 2nd sialic acid-binding site resulting in reduced neuraminidase activity preceded emergence of H7N9 influenza a virus. J. Virol. (2017)Google Scholar
- 26.Kedzierski, L., et al.: Suppressor of cytokine signaling (SOCS)5 ameliorates influenza infection via inhibition of EGFR signaling. Elife (2017)Google Scholar