Abstract
Meningitis is one of the pandemic diseases that many less developed countries suffer, primarily due to the lack of economic resources to face it. The more severe types of meningitis, Meningococcal Disease, MD, demand immediate medical attention since delays increase the risk of mortality. This paper presents an open and integrated Clinical Decision Support System to assist physicians in the different stages of meningitis diagnostics through observable symptoms. Our system integrates three intelligent components which try to give support to physicians in early diagnostics of meningitis. These components are based on interpretable tree-based machine learning models and knowledge-engineering techniques. A dataset of 26,228 records of patients with a meningitis diagnosis in Brazil was used to construct and evaluate the system. The performance indicators of the decision models exhibit an outstanding classification performance for MD meningitis with a classification accuracy of 94.3%. In order to test the correct diagnosis of the system, an evaluation study with real patients' data was performed. The experimental results concluded that excluding meningitis cases based only on observable symptoms is much more complicated than diagnosing it. However, the system properly diagnosed 88% of meningitis cases from the real database.
Highlights
Meningitis is a severe infectious disease which can be caused by several microorganisms such as viruses, bacteria, parasites, and fungi [1]
CDSS EVALUATION AND RESULTS Once the best decision models for Decision Model 1 (DM1), Decision Model 2 (DM2) and Decision Model 3 (DM3) had been determined, they were integrated into our CDSS in the Triage, Meningococcal Disease and Aetiological agents
The performance evaluation of our CDSS was accomplished in the following way: Initially, we generated the patients according to their information stored in the datasets
Summary
Meningitis is a severe infectious disease which can be caused by several microorganisms such as viruses, bacteria, parasites, and fungi [1]. As reported by the WHO (World Health Organization), the impact of meningitis is most significant among underprivileged populations In this sense, a higher incidence of Meningococcal Disease (MD), i.e. a severe type of meningitis, is linked to factors such as poor living conditions and overcrowded housing. A higher incidence of Meningococcal Disease (MD), i.e. a severe type of meningitis, is linked to factors such as poor living conditions and overcrowded housing These severe types of meningitis demand immediate medical attention since delays increase the risk of mortality. The problem with diagnosing and treating meningitis in less developed countries is that rural and isolated areas often do not have the required resources to undertake laboratory tests early on For this reason, they are compelled to recommend immediate hospitalisation of patients who exhibit some symptoms of meningitis, which, in turn, leads to. Bulging fontanelle must be checked in children under one year
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