Adoption and utilization of medical decision support systems in the diagnosis of febrile Diseases: A systematic literature review

Khan, Nayab; Okoli, Chinyere Nwafor; Ekpin, Victory; Attai, Kingsley; Nwokoro, Chukwudi; Sabi, Humphrey M.; Akwaowo, Christie Divine; Osuji, Joseph; Benavente, Luis; Uzoka, Faith-Michael E.

doi:10.1016/j.eswa.2023.119638

Cited by 8 publications

(1 citation statement)

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“…MDSS based on FCM has the potential to assist medical doctors, nurses, FHWs, and patients with some ethical concerns. MDSSs play a significant role in promoting an efficient and effective healthcare system [ 45 ]. To this end, a number of ethical issues that follow the use of intelligent machine diagnoses were followed in the process of data collection, planning, design, and development of this study.…”

Section: Resultsmentioning

confidence: 99%

Modelling Differential Diagnosis of Febrile Diseases with Fuzzy Cognitive Map

Obot

John²,

Udo

et al. 2023

TropicalMed

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The report of the World Health Organization (WHO) about the poor accessibility of people living in low-to-middle-income countries to medical facilities and personnel has been a concern to both professionals and nonprofessionals in healthcare. This poor accessibility has led to high morbidity and mortality rates in tropical regions, especially when such a disease presents itself with confusable symptoms that are not easily differentiable by inexperienced doctors, such as those found in febrile diseases. This prompted the development of the fuzzy cognitive map (FCM) model to serve as a decision-support tool for medical health workers in the diagnosis of febrile diseases. With 2465 datasets gathered from four states in the febrile diseases-prone regions in Nigeria with the aid of 60 medical doctors, 10 of those doctors helped in weighting and fuzzifying the symptoms, which were used to generate the FCM model. Results obtained from computations to predict diagnosis results for the 2465 patients, and those diagnosed by the physicians on the field, showed an average of 87% accuracy for the 11 febrile diseases used in the study. The number of comorbidities of diseases with varying degrees of severity for most patients in the study also covary strongly with those found by the physicians in the field.

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Section: Resultsmentioning

confidence: 99%