An automated screening system for tuberculosis Articles uri icon

publication date

  • May 2014

start page

  • 855

end page

  • 862

issue

  • 3

volume

  • 18

international standard serial number (ISSN)

  • 2168-2194

electronic international standard serial number (EISSN)

  • 2168-2208

abstract

  • Automated screening systems are commonly used to detect some agent in a sample and take a global decision about the subject (e.g., ill/healthy) based on these detections. We propose a Bayesian methodology for taking decisions in (sequential) screening systems that considers the false alarm rate of the detector. Our approach assesses the quality of its decisions and provides lower bounds on the achievable performance of the screening system from the training data. In addition, we develop a complete screening system for sputum smears in tuberculosis diagnosis, and show, using a real-world database, the advantages of the proposed framework when compared to the commonly used count detections and threshold approach.

keywords

  • diseases; medical diagnostic computing; medical expert systems; patient diagnosis