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Development of two artificial neural network models to support the diagnosis of pulmonary tuberculosis in hospitalized patients in Rio de Janeiro, Brazil

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Abstract

Pulmonary tuberculosis (PTB) remains a worldwide public health problem. Diagnostic algorithms to identify the best combination of diagnostic tests for PTB in each setting are needed for resource optimization. We developed one artificial neural network model for classification (multilayer perceptron—MLP) and another risk group assignment (self-organizing map—SOM) for PTB in hospitalized patients in a high complexity hospital in Rio de Janeiro City, using clinical and radiologic data collected from 315 presumed PTB cases admitted to isolation rooms from March 2003 to December 2004 (TB prevalence = 21.5 %). The MLP model included 7 variables—radiologic classification, age, gender, cough, night sweats, weight loss and anorexia. The sensitivity of the MLP model was 96.0 % (95 % CI ±2.0), the specificity was 89.0 % (95 % CI ±2.0), the positive predictive value was 72.5 % (95 % CI ±3.5) and the negative predictive value was 98.5 % (95 % CI ±0.5). The variable with the highest discriminative power was the radiologic classification. The high negative predictive value found in the MLP model suggests that the use of this model at the moment of hospital admission is safe. SOM model was able to correctly assign high-, medium- and low-risk groups to patients. If prospective validation in other series is confirmed, these models can become a tool for decision-making in tertiary health facilities in countries with limited resources.

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Acknowledgments

This work was supported by Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) Process: Nº E: 26/111608/2008. AK, JMS and FCQM are recipients of a career award from Conselho Nacional de Desenvolvimento CIentífico e Tecnológico (CNPq) (produtividade em pesquisa) and FAPERJ (Cientistas do Nosso Estado). The funders had no role in study design, data collection and analysis, the decision to publish, or the preparation of the manuscript.

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Authors and Affiliations

  1. Instituto de Doenças do Tórax (IDT)/Clementino Fraga Filho Hospital (CFFH), Federal University of Rio de Janeiro, Rua Professor Rodolpho Paulo Rocco, n° 255 - 6° Andar - Cidade Universitária - Ilha do Fundão, 21941-913, Rio De Janeiro, Brazil

    Fábio S. Aguiar, Afrânio L. Kritski & Fernanda C. Q. Mello

  2. Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia/Poli, Federal University of Rio de Janeiro, Rio De Janeiro, Brazil

    Rodrigo C. Torres, João V. F. Pinto & José M. Seixas

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  1. Fábio S. Aguiar
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  2. Rodrigo C. Torres
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  3. João V. F. Pinto
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  4. Afrânio L. Kritski
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  5. José M. Seixas
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  6. Fernanda C. Q. Mello
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Correspondence to Fábio S. Aguiar.

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Aguiar, F.S., Torres, R.C., Pinto, J.V.F. et al. Development of two artificial neural network models to support the diagnosis of pulmonary tuberculosis in hospitalized patients in Rio de Janeiro, Brazil. Med Biol Eng Comput 54, 1751–1759 (2016). https://doi.org/10.1007/s11517-016-1465-1

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