Re-defining exacerbations of chronic obstructive pulmonary disease and developing tools to characterise heterogeneity and treatment response

Thesis

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. Progressive in nature, COPD is punctuated by acute periods of worsening termed ‘exacerbations’, a cause of morbidity and mortality. Exacerbations are heterogenous with respect to symptoms and inflammatory biology.

In this thesis, mathematical methods were researched for their appropriateness to characterise COPD heterogeneity and predict treatment response. Algorithms were developed to predi...

Expand abstract

Authors: Halner, A

• Alternative title: Re-defining exacerbations of chronic obstructive pulmonary disease and developing tools to characterise heterogeneity and treatment response
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• DOI: 10.5287/ora-1r84r8vrp
• Language: English
• Keywords: Artificial Intelligence; Chronic Obstructive Pulmonary Disease; Cluster Analysis; Statistics; Disease Trajectories; Trajectories; Supervised Learning; AI; Machine learning; Data Science; Crises; Clinical Machine Learning; Asthma; Predictive Modelling; Personalised Medicine; COPD; ML; Phenotyping; Predictive Analytics; Endotyping; Exacerbations; Respiratory Medicine; Mathematical Modelling
• Subjects: Personalised Medicine; Machine Learning; Mathematics; Data Science; Lungs--Diseases, Obstructive; Statistics