Using Computational Simulations Based on Fuzzy Cognitive Maps to Detect Dengue Complications

Overview

Journal Diagnostics (Basel)

Specialty Radiology

Date 2024 Mar 13

PMID 38473004

Authors

William Hoyos

Kenia Hoyos

Rander Ruiz

Affiliations

Soon will be listed here.

Abstract

Dengue remains a globally prevalent and potentially fatal disease, affecting millions of people worldwide each year. Early and accurate detection of dengue complications is crucial to improving clinical outcomes and reducing the burden on healthcare systems. In this study, we explore the use of computational simulations based on fuzzy cognitive maps (FCMs) to improve the detection of dengue complications. We propose an innovative approach that integrates clinical data into a computational model that mimics the decision-making process of a medical expert. Our method uses FCMs to model complexity and uncertainty in dengue. The model was evaluated in simulated scenarios with each of the dengue classifications. These maps allow us to represent and process vague and fuzzy information effectively, capturing relationships that often go unnoticed in conventional approaches. The results of the simulations show the potential of our approach to detecting dengue complications. This innovative strategy has the potential to transform the way clinical management of dengue is approached. This research is a starting point for further development of complication detection approaches for events of public health concern, such as dengue.

References

Wong P, Wong L, AbuBakar S . Diagnosis of severe dengue: Challenges, needs and opportunities. J Infect Public Health. 2019; 13(2):193-198. DOI: 10.1016/j.jiph.2019.07.012. View

Huang S, Tsai H, Hung S, Ko W, Wang J . Assessing the risk of dengue severity using demographic information and laboratory test results with machine learning. PLoS Negl Trop Dis. 2020; 14(12):e0008960. PMC: 7757819. DOI: 10.1371/journal.pntd.0008960. View

Asish P, Dasgupta S, Rachel G, Bagepally B, Girish Kumar C . Global prevalence of asymptomatic dengue infections - a systematic review and meta-analysis. Int J Infect Dis. 2023; 134:292-298. DOI: 10.1016/j.ijid.2023.07.010. View

Davi C, Pastor A, Oliveira T, Neto F, Braga-Neto U, Bigham A . Severe Dengue Prognosis Using Human Genome Data and Machine Learning. IEEE Trans Biomed Eng. 2019; 66(10):2861-2868. DOI: 10.1109/TBME.2019.2897285. View

Wagle A, Hegde S, Sanjay S, Eong K . Ophthalmic manifestations in seropositive dengue fever patients during epidemics caused by predominantly different dengue serotypes. Adv Ophthalmol Pract Res. 2023; 2(2):100049. PMC: 10577814. DOI: 10.1016/j.aopr.2022.100049. View

Htun T, Xiong Z, Pang J . Clinical signs and symptoms associated with WHO severe dengue classification: a systematic review and meta-analysis. Emerg Microbes Infect. 2021; 10(1):1116-1128. PMC: 8205005. DOI: 10.1080/22221751.2021.1935327. View

Zhang S, Wang S, Zhang J, Dong D, Mu W, Xia X . Artificial intelligence-based computer-aided diagnosis system supports diagnosis of lymph node metastasis in esophageal squamous cell carcinoma: A multicenter study. Heliyon. 2023; 9(3):e14030. PMC: 10009687. DOI: 10.1016/j.heliyon.2023.e14030. View

Saul L, Sanfeliciano A, Botella L, Perea R, Gonzalez-Puerto J . Fuzzy Cognitive Maps as a Tool for Identifying Cognitive Conflicts That Hinder the Adoption of Healthy Habits. Int J Environ Res Public Health. 2022; 19(3). PMC: 8835037. DOI: 10.3390/ijerph19031411. View

Venkatesh A, Patel R, Goyal S, Rajaratnam T, Sharma A, Hossain P . Ocular manifestations of emerging viral diseases. Eye (Lond). 2021; 35(4):1117-1139. PMC: 7844788. DOI: 10.1038/s41433-020-01376-y. View

10.

Hoyos W, Aguilar J, Toro M . A clinical decision-support system for dengue based on fuzzy cognitive maps. Health Care Manag Sci. 2022; 25(4):666-681. DOI: 10.1007/s10729-022-09611-6. View

11.

Yuan K, Chen Y, Zhong M, Lin Y, Liu L . Risk and predictive factors for severe dengue infection: A systematic review and meta-analysis. PLoS One. 2022; 17(4):e0267186. PMC: 9012395. DOI: 10.1371/journal.pone.0267186. View

12.

Procopio A, Colletta S, Laratta E, Mellace M, Tilocca B, Ceniti C . Integrated One Health strategies in Dengue. One Health. 2024; 18:100684. PMC: 11247296. DOI: 10.1016/j.onehlt.2024.100684. View

13.

Caicedo-Borrero D, Tovar J, Mendez A, Parra B, Bonelo A, Celis J . Development and Performance of Dengue Diagnostic Clinical Algorithms in Colombia. Am J Trop Med Hyg. 2020; 102(6):1226-1236. PMC: 7253082. DOI: 10.4269/ajtmh.19-0722. View

14.

Ming D, Tuan N, Hernandez B, Sangkaew S, Vuong N, Chanh H . The Diagnosis of Dengue in Patients Presenting With Acute Febrile Illness Using Supervised Machine Learning and Impact of Seasonality. Front Digit Health. 2022; 4:849641. PMC: 8963938. DOI: 10.3389/fdgth.2022.849641. View

15.

Khan M, Yang Z, Lin C, Hsu M, Urbina A, Assavalapsakul W . Dengue overview: An updated systemic review. J Infect Public Health. 2023; 16(10):1625-1642. DOI: 10.1016/j.jiph.2023.08.001. View

16.

Chowdhury S, Sayeed S, Rashid I, Alam M, Masum A, Dewan M . Shapley-Additive-Explanations-Based Factor Analysis for Dengue Severity Prediction using Machine Learning. J Imaging. 2022; 8(9). PMC: 9506144. DOI: 10.3390/jimaging8090229. View

17.

Sangkaew S, Ming D, Boonyasiri A, Honeyford K, Kalayanarooj S, Yacoub S . Risk predictors of progression to severe disease during the febrile phase of dengue: a systematic review and meta-analysis. Lancet Infect Dis. 2021; 21(7):1014-1026. PMC: 8240557. DOI: 10.1016/S1473-3099(20)30601-0. View

18.

Moallemi S, Lloyd A, Rodrigo C . Early biomarkers for prediction of severe manifestations of dengue fever: a systematic review and a meta-analysis. Sci Rep. 2023; 13(1):17485. PMC: 10576797. DOI: 10.1038/s41598-023-44559-9. View

19.

Thach T, Eisa H, Hmeda A, Faraj H, Thuan T, Abdelrahman M . Predictive markers for the early prognosis of dengue severity: A systematic review and meta-analysis. PLoS Negl Trop Dis. 2021; 15(10):e0009808. PMC: 8519480. DOI: 10.1371/journal.pntd.0009808. View

20.

Zargari Marandi R, Leung P, Sigera C, Murray D, Weeratunga P, Fernando D . Development of a machine learning model for early prediction of plasma leakage in suspected dengue patients. PLoS Negl Trop Dis. 2023; 17(3):e0010758. PMC: 10035900. DOI: 10.1371/journal.pntd.0010758. View