Exacerbation predictive modelling using real-world data from the myCOPD app

Henry M.G. Glyde; Alison Blythin; Tom Wilkinson; Ian Nabney; James  Dodd

doi:10.1016/j.heliyon.2024.e31201

Exacerbation predictive modelling using real-world data from the myCOPD app

Henry M.G. Glyde^*, Alison Blythin, Tom Wilkinson, Ian Nabney, James Dodd

^*Corresponding author for this work

Research output: Contribution to journal › Article (Academic Journal) › peer-review

Abstract

Acute exacerbations of COPD (AECOPD) are episodes of breathlessness, cough and sputum which are associated with the risk of hospitalisation, progressive lung function decline and death. They are often missed or diagnosed late. Accurate timely intervention can improve these poor outcomes. Digital tools can be used to capture symptoms and other clinical data in COPD. This study aims to apply machine learning to the largest available real-world digital dataset to develop AECOPD Prediction tools which could be used to support early intervention and improve clinical outcomes.

Original language	English
Article number	e31201
Number of pages	9
Journal	Heliyon
Volume	10
Issue number	10
Early online date	13 May 2024
DOIs	https://doi.org/10.1016/j.heliyon.2024.e31201
Publication status	Published - 30 May 2024

Bibliographical note

Publisher Copyright:
© 2024 The Authors

Research Groups and Themes

Digital Health
Intelligent Systems Laboratory
Academic Respiratory Unit

Keywords

machine learning
COPD Exacerbations
self-management

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title = "Exacerbation predictive modelling using real-world data from the myCOPD app",

abstract = "Acute exacerbations of COPD (AECOPD) are episodes of breathlessness, cough and sputum which are associated with the risk of hospitalisation, progressive lung function decline and death. They are often missed or diagnosed late. Accurate timely intervention can improve these poor outcomes. Digital tools can be used to capture symptoms and other clinical data in COPD. This study aims to apply machine learning to the largest available real-world digital dataset to develop AECOPD Prediction tools which could be used to support early intervention and improve clinical outcomes.",

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TY - JOUR

T1 - Exacerbation predictive modelling using real-world data from the myCOPD app

AU - Glyde, Henry M.G.

AU - Blythin, Alison

AU - Wilkinson, Tom

AU - Nabney, Ian

AU - Dodd, James

PY - 2024/5/30

Y1 - 2024/5/30

N2 - Acute exacerbations of COPD (AECOPD) are episodes of breathlessness, cough and sputum which are associated with the risk of hospitalisation, progressive lung function decline and death. They are often missed or diagnosed late. Accurate timely intervention can improve these poor outcomes. Digital tools can be used to capture symptoms and other clinical data in COPD. This study aims to apply machine learning to the largest available real-world digital dataset to develop AECOPD Prediction tools which could be used to support early intervention and improve clinical outcomes.

AB - Acute exacerbations of COPD (AECOPD) are episodes of breathlessness, cough and sputum which are associated with the risk of hospitalisation, progressive lung function decline and death. They are often missed or diagnosed late. Accurate timely intervention can improve these poor outcomes. Digital tools can be used to capture symptoms and other clinical data in COPD. This study aims to apply machine learning to the largest available real-world digital dataset to develop AECOPD Prediction tools which could be used to support early intervention and improve clinical outcomes.

KW - machine learning

KW - COPD Exacerbations

KW - self-management

U2 - 10.1016/j.heliyon.2024.e31201

DO - 10.1016/j.heliyon.2024.e31201

M3 - Article (Academic Journal)

C2 - 38803869

SN - 2405-8440

VL - 10

JO - Heliyon

JF - Heliyon

IS - 10

M1 - e31201

ER -

Exacerbation predictive modelling using real-world data from the myCOPD app

Abstract

Bibliographical note

Research Groups and Themes

Keywords

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