Case studies of clinical decision-making through prescriptive models based on machine learning

Hoyos, William; Aguilar, Jose; Raciny, Mayra; Toro, Mauricio

doi:10.1016/j.cmpb.2023.107829

Cited by 2 publications

(1 citation statement)

References 39 publications

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“…Other studies have used clinical data, routine laboratory tests, and risk factors. For example, Hoyos et al [ 24 ] combined artificial neural networks and support vector machines to classify the patient based on dengue severity, achieving 98% accuracy. Yang et al [ 20 ] developed a classification model to find key risk factors in severe dengue cases; the results showed that the probability of the occurrence of severe dengue cases ranged from 7% (age > 12.5 years, without plasma leakage) to 92.9% (age ≤ 12.5 years, with dyspnea and plasma leakage).…”

Section: Related Workmentioning

confidence: 99%

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

Hoyos,

Ruíz

2024

Diagnostics

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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.

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Section: Related Workmentioning

confidence: 99%