Abstract
Meningitis diagnostic is a challenge especially in less developed countries where medical resources are limited, and the cost of treatments are not always affordable. For this reason, it would be desirable to have available any solution that could perform early diagnostics on meningitis to find the suitable treatment, at least for the more severe types of this disease (bacterial, meningococcal, …). In this paper, we present a set of clinical decision support models to assist physicians in the meningitis diagnostics. These models try to answer to the following two research questions: Can it be diagnosed reliably if a patient has meningitis? Can it be determined whether it is a bacterial or aseptic case? To explore the performance of our models, we have conducted validation experiments with a dataset of patients. For this purpose, we have counted with data of patient meningitis diagnostics in Brazil. The database was provided by the Directorate of Health Information of the Secretary of Health of the Brazilian State of Bahia and contained over 16,000 records. Several indexes have been computed to show the model accuracy, but the best corresponds to the ADTree classifier with a precision of 0.859 and a ROC area over 0.86. Validation results show a good performance of the models, suggesting, therefore, that our proposal can effectively support physicians’ decisions on meningitis management and treatment.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Tunkel, A.R., et al.: Practice guidelines for the management of bacterial meningitis. Clin. Infect. Dis. 39(9), 1267–1284 (2004). https://doi.org/10.1086/425368
WHO: World Health Organization: Meningococcal meningitis. Fact sheet N°141 (2015)
GES: Brasil, Ministéio da Saùde, Secretaria de Vigilância em Saùde. Guide to Epidemiological Surveillance. 7th edn. Chapter 12, pp. 21–47 (2009). http://bvsms.saude.gov.br/bvs/publicacoes/guia_vigilancia_epidemiologica_7ed.pdf
Lelis, V.M., Guzmán, E., Belmonte, M.V.: A Statistical Classifier to Support Diagnose Meningitis in Less Developed Areas of Brazil. J. Med. Systems 41, 145 (2017)
Ozaydin, B., Hardin, J.M., Chhieng, D.C.: Data mining and clinical decision support systems. In: Berner, E. (ed.) Clinical Decision Support Systems. Health Informatics, pp. 45–68. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31913-1_3
Shortliffe, E.H., Davis, R., Axline, S.G., Buchanan, B.G., Green, C.C., Cohen, S.N.: Computer-based consultations in clinical therapeutics: explanation and rule acquisition capabilities of the MYCIN system. Comput. Biomed. Res. 1975(8), 303–320 (1975)
Shirabad, J.S., Wilk, S., Michalowski, W., Farion, K.: Implementing an integrative multi-agent clinical decision support system with open source software. J. Med. Syst. 36(1), 123–137 (2012)
Han, J., Rodriguez, J.C., Beheshti, M.: Discovering decision tree based diabetes prediction model. In: Kim, T., Fang, W.C., Lee, C., Arnett, K.P. (eds.) ASEA 2008. Communications in Computer and Information Science, vol. 30. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-10242-4_9
Farion, K., Michalowski, W., Wilk, S., O’Sullivan, D., Matwin, S.: A tree-based decision model to support prediction of the severity of asthma exacerbations in children. J. Med. Syst. 34(4), 551–562 (2010)
Alickovic, E., Subasi, A.: Medical decision support system for diagnosis of heart arrhythmia using DWT and random forests classifier. J. Med. Syst. 40(4), 108 (2016)
Huang, M.L., Chen, H.Y.: Glaucoma classification model based on GDx VCC measured parameters by decision tree. J. Med. Syst. 34(6), 1141–1147 (2010)
Ting, H., Mai, Y.T., Hsu, H.C., Wu, H.C., Tseng, M.H.: Decision tree based diagnostic system for moderate to severe obstructive sleep apnea. J. Med. Syst. 38(9), 94 (2014)
Chao, C.M., Yu, Y.W., Cheng, B.W., Kuo, Y.L.: Construction the model on the breast cancer survival analysis use support vector machine, logistic regression and decision tree. J. Med. Syst. 38(10), 106 (2014)
Abdar, M., Zomorodi-Moghadam, M., Das, R., Ting, I.H.: Performance analysis of classification algorithms on early detection of liver disease. Expert Syst. Appl. 67, 239–251 (2017)
Yeh, D.Y., Cheng, C.H., Chen, Y.W.: A predictive model for cerebrovascular disease using data mining. Expert Syst. Appl. 38(7), 8970–8977 (2011)
Mago, V.K., Mehta, R., Woolrych, R., Papageorgiou, E.: Supporting meningitis diagnosis amongst infants and children through the use of fuzzy cognitive maps. BMC Med. Inform. Decis. Mak. 12, 98 (2012)
Ocampo, E., Maceiras, M., Herrera, S., Maurente, C., Rodríguez, D., Sicilia, M.A.: Comparing Bayesian inference case-based reasoning as support techniques in the diagnosis of Acute Bacterial Meningitis. Expert Syst. Appl. 38, 10343–10354 (2011)
Revett, K., Gorunescu, F., Goronesu, M., Ene, M.: A machine learning approach to differentiating bacterial from viral meningitis. In: IEEE International Symposium on Modern Computing (2006)
Gowin, E., Januszkliewicz-Lewandowska, D., Slowinski, R., Blaszczynski, J., Michalak, M., Wysocki, J.: With a little help from a computer: discriminating between bacterial and viral meningitis based on dominance-based rough set approach analysis. Medicine 96, 32 (2017)
Weitzel, L., Teixeira de Assis, J., Soares, A.: Medical training simulation system to assist novice physicians in diagnostics problem solving. In: Proceedings of the 6th WSEAS International Conference on Neural Networks, Lisbon, pp. 239–243 (2005)
TNS: Technical note SUS: Case definition and epidemiological surveillance, Josué Laguardia and Maria Lúcia Penna. Inf. Epidemiol. SUS v.8 n.4, Brasília, December 1999
Fawcett, T.: ROC graphs: notes and practical considerations for researchers. Mach. Learn. 31(1), 1–38 (2004)
Mejía, G., Ramelli, M.: Interpretación clínica del laboratorio. Ed. Médica Panam (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Lelis, V.M., Belmonte, MV., Guzmán, E. (2018). Decision Support Models to Assist in the Diagnosis of Meningitis. In: Faron Zucker, C., Ghidini, C., Napoli, A., Toussaint, Y. (eds) Knowledge Engineering and Knowledge Management. EKAW 2018. Lecture Notes in Computer Science(), vol 11313. Springer, Cham. https://doi.org/10.1007/978-3-030-03667-6_35
Download citation
DOI: https://doi.org/10.1007/978-3-030-03667-6_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03666-9
Online ISBN: 978-3-030-03667-6
eBook Packages: Computer ScienceComputer Science (R0)