title: Development of two artificial neural network models to support the diagnosis of pulmonary tuberculosis in hospitalized patients in Rio de Janeiro, Brazil.
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.
authors.affiliation: 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. aguiarmd@gmail.com.
authors.forename: Fábio S
authors.identifier: http://orcid.org/0000-0002-9145-0925
authors.initials: FS
authors.lastname: Aguiar
authors.affiliation: Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia/Poli, Federal University of Rio de Janeiro, Rio De Janeiro, Brazil.
authors.forename: Rodrigo C
authors.initials: RC
authors.lastname: Torres
authors.affiliation: Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia/Poli, Federal University of Rio de Janeiro, Rio De Janeiro, Brazil.
authors.forename: João V F
authors.initials: JV
authors.lastname: Pinto
authors.affiliation: 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.
authors.forename: Afrânio L
authors.initials: AL
authors.lastname: Kritski
authors.affiliation: Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia/Poli, Federal University of Rio de Janeiro, Rio De Janeiro, Brazil.
authors.forename: José M
authors.initials: JM
authors.lastname: Seixas
authors.affiliation: 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.
authors.forename: Fernanda C Q
authors.initials: FC
authors.lastname: Mello
mesh_terms: D000328:Adult; D001938:Brazil; D005260:Female; D006760:Hospitalization; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity; D014397:Tuberculosis, Pulmonary / Q000175:diagnosis*
keywords: Artificial neural networks; Decision support systems; Diagnosis; Tuberculosis