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In 2005 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) started a collaborative research project to develop a Decision Support System for Smear Negative Pulmonary Tuberculosis (SNPT). The motivation was 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. The project also aims at increasing the scientific and technological capacity in the country for the progress of new technologies incorporation in the public and private system as well revising public policies to control tuberculosis. The conception of the initiative was based on previous experiences of all participants and innovative proposals applied to known restrictions. The initial step was the merging of mathematical modeling and information management through the Web. Paper forms to acquire patient’s information were substituted by digital ones. In this way, all data could rapidly be accessed and available online. TB experts defined the data inputs and were responsible to validate the system and its outputs. Data quality methods were used to guarantee the accuracy of records, avoiding uncertain information and improving the value of the final result. Finally, portability was a mandatory requirement in order to guarantee the use of the system in different regions of the Country. The use of symptomatic information to feed a neural network model in order to build the decision support system would guarantee a reliable proposal, constructed with low cost resources.

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