Clinical decision support for screening, diagnosis and assessment of respiratory diseases: Chronic obstructive pulmonary disease as a use case.

Doctoral thesisClinical decision support for screening, diagnosis and assessment of respiratory diseases: Chronic obstructive pulmonary disease as a use case.

 

By Filip Velickovski

 

Supervised by Dr. Robert Martí Marly / Dr. Luigi Ceccaroni  

 

 

Abstract

 

img1-thesisClinical decision support systems (CDSS), which aim to provide healthcare staff with patient specific advice, can enhance the level of care delivered to citizens by offering access to advanced medical knowledge in non-specialist healthcare settings. The work presented in this thesis adapts, refines and contributes to methodologies in applied clinical decision support research, through a concrete use-case of Chronic Obstructive Pulmonary Disease (COPD) at the early stage, with the outlook of generalising these methods to a broader set of chronic respiratory diseases, and other non-commutable diseases.

 

 

img2-thesisChronic obstructive pulmonary disease is a major cause of chronic morbidity and mortality worldwide, and along with other chronic repository diseases currently represents a high burden on global healthcare systems. The primary objective of this thesis is to facilitate through clinical decision support research and development, the clinical tasks related to early stage COPD detection.

 

 

img3-thesisIn this thesis we propose a framework for designing, developing, a CDSS offering a suite of services for the early detection and assessment of COPD, and then demonstrate how these services can be integrated into the work-flow of healthcare providers. Furthermore, we focus on supporting spirometry, one of the main diagnostic tools in respiratory disease assessment. We present two methods to offer decision support in assuring the quality of a spirometry test that can be easily embedded into the CDSS framework. The first method is a novel algorithm that relies on a set of rules operating on 23 new parameters to define a high quality test. The second is a machine-learning approach, where we optimise the distinction between a good quality spirometry test and a poor one using a set of supervised-learning classifiers and hyper-parameters.

 

 

The application of the outcomes generated from this research has a credible potential to contribute to lowering the level of under-diagnosis, reducing the level of misdiagnosis, and improving the quality of lung function assessment performed in non-specialist settings for COPD as well as other chronic respiratory diseases.

 

 

 

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