Bayesian models for screening and diagnosis of pulmonary disease

Pulmonary and respiratory diseases comprise a large proportion of the global disease burden, responsible for both mortality and disability, with the most common ailments being asthma, chronic obstructive pulmonary disorder (COPD), and allergic rhinitis (AR). This burden is especially concentrated in the developing world, where resources for diagnosing these diseases are more limited. In India, COPD recently became the second leading cause of death. Health workers and many general practitioner doctors are not trained to diagnose pulmonary diseases, leading to high rates of misdiagnosis and underdiagnosis. Over the past six years, our group has been developing screening tools for pulmonary disease. We have developed a mobile toolkit that consists of an electronic stethoscope, an augmented reality peak flow meter, and an electronic questionnaire. Previously, logistic regression has been used for modeling pulmonary disease.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Anand, Aneesh(Aneesh M.)

Advisor dc:contributor.advisor

• Richard R. Fletcher.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/121618

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/121618