A Decision Support System Based on Artificial Neural Networks for Pulmonary Tuberculosis Diagnosis

Maidantchik, C.; Seixas, J. M.; Grael, Felipe F.; Torres, Rodrigo Coura; Ferreira, Fernando G. D. C.; Gomes, Andressa Silva; Faier, José M.; Silva, José Roberto Lapa e; Mello, Fernanda Cq; Kritski, Afrânio Lineu; Filho, João Baptista de Oliveira e Souza

doi:10.5772/19834

Cited by 1 publication

(1 citation statement)

References 15 publications

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“…The Faculty of Medicine, the Electronic and Computing Engineering Department of the Federal University of Rio de Janeiro (UFRJ) and the Electrical Engineering Department of the Federal Center of Technological Education (CEFET-RJ) worked on a collaborative research project to develop a decision support system for smear negative pulmonary tuberculosis (SNPT) [ 22 ]. This project aims to develop, through a multi-disciplinary, multi-institutional, innovative and cost-effective approach, new paradigms to prevent the disease progression and support the rapid evaluation of new therapies.…”

Section: Introductionmentioning

confidence: 99%

A decision support system to determine optimal ventilator settings

Akbulut

Akkur

Akan

et al. 2014

BMC Med Inform Decis Mak

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BackgroundChoosing the correct ventilator settings for the treatment of patients with respiratory tract disease is quite an important issue. Since the task of specifying the parameters of ventilation equipment is entirely carried out by a physician, physician’s knowledge and experience in the selection of these settings has a direct effect on the accuracy of his/her decisions. Nowadays, decision support systems have been used for these kinds of operations to eliminate errors. Our goal is to minimize errors in ventilation therapy and prevent deaths caused by incorrect configuration of ventilation devices. The proposed system is designed to assist less experienced physicians working in the facilities without having lung mechanics like cottage hospitals.MethodsThis article describes a decision support system proposing the ventilator settings required to be applied in the treatment according to the patients’ physiological information. The proposed model has been designed to minimize the possibility of making a mistake and to encourage more efficient use of time in support of the decision making process while the physicians make critical decisions about the patient. Artificial Neural Network (ANN) is implemented in order to calculate frequency, tidal volume, FiO2 outputs, and this classification model has been used for estimation of pressure support / volume support outputs. For the obtainment of the highest performance in both models, different configurations have been tried. Various tests have been realized for training methods, and a number of hidden layers mostly affect factors regarding the performance of ANNs.ResultsThe physiological information of 158 respiratory patients over the age of 60 and were treated in three different hospitals between the years 2010 and 2012 has been used in the training and testing of the system. The diagnosed disease, core body temperature, pulse, arterial systolic pressure, diastolic blood pressure, PEEP, PSO2, pH, pCO2, bicarbonate data as well as the frequency, tidal volume, FiO2, and pressure support / volume support values suitable for use in the ventilator device have been recommended to the physicians with an accuracy of 98,44%. Performed experiments show that sequential order weight/bias training was found to be the most ideal ANN learning algorithm for regression model and Bayesian regulation backpropagation was found to be the most ideal ANN learning algorithm for classification models.ConclusionsThis article aims at making independent of the choice of parameters from physicians in the ventilator treatment of respiratory tract patients with proposed decision support system. The rate of accuracy in prediction of systems increases with the use of data of more patients in training. Therefore, non-physician operators can use systems in determination of ventilator settings in case of emergencies.

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Section: Introductionmentioning

confidence: 99%