Automatic generation of personalised alert thresholds for patients with COPD

Chronic Obstructive Pulmonary Disease (COPD) is a chronic disease predicted to become the third leading cause of death by 2030. Patients with COPD are at risk of exacerbations in their symptoms, which have an adverse effect on their quality of life and may require emergency hospital admission. Using...

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Những tác giả chính: Velardo, C, Shah, SA, Gibson, O, Rutter, H, Farmer, A, Tarassenko, L
Định dạng: Conference item
Được phát hành: European Signal Processing Conference, EUSIPCO 2014
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author Velardo, C
Shah, SA
Gibson, O
Rutter, H
Farmer, A
Tarassenko, L
author_facet Velardo, C
Shah, SA
Gibson, O
Rutter, H
Farmer, A
Tarassenko, L
author_sort Velardo, C
collection OXFORD
description Chronic Obstructive Pulmonary Disease (COPD) is a chronic disease predicted to become the third leading cause of death by 2030. Patients with COPD are at risk of exacerbations in their symptoms, which have an adverse effect on their quality of life and may require emergency hospital admission. Using the results of a pilot study of an m-Health system for COPD self-management and tele-monitoring, we demonstrate a data-driven approach for computing personalised alert thresholds to prioritise patients for clinical review. Univariate and multivariate methodologies are used to analyse and fuse daily symptom scores, heart rate, and oxygen saturation measurements. We discuss the benefits of a multivariate kernel density estimator which improves on univariate approaches.
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spelling oxford-uuid:d009d356-e2dc-4cb4-9adc-9f02d26acebc2022-03-27T07:47:02ZAutomatic generation of personalised alert thresholds for patients with COPDConference itemhttp://purl.org/coar/resource_type/c_5794uuid:d009d356-e2dc-4cb4-9adc-9f02d26acebcSymplectic Elements at OxfordEuropean Signal Processing Conference, EUSIPCO2014Velardo, CShah, SAGibson, ORutter, HFarmer, ATarassenko, LChronic Obstructive Pulmonary Disease (COPD) is a chronic disease predicted to become the third leading cause of death by 2030. Patients with COPD are at risk of exacerbations in their symptoms, which have an adverse effect on their quality of life and may require emergency hospital admission. Using the results of a pilot study of an m-Health system for COPD self-management and tele-monitoring, we demonstrate a data-driven approach for computing personalised alert thresholds to prioritise patients for clinical review. Univariate and multivariate methodologies are used to analyse and fuse daily symptom scores, heart rate, and oxygen saturation measurements. We discuss the benefits of a multivariate kernel density estimator which improves on univariate approaches.
spellingShingle Velardo, C
Shah, SA
Gibson, O
Rutter, H
Farmer, A
Tarassenko, L
Automatic generation of personalised alert thresholds for patients with COPD
title Automatic generation of personalised alert thresholds for patients with COPD
title_full Automatic generation of personalised alert thresholds for patients with COPD
title_fullStr Automatic generation of personalised alert thresholds for patients with COPD
title_full_unstemmed Automatic generation of personalised alert thresholds for patients with COPD
title_short Automatic generation of personalised alert thresholds for patients with COPD
title_sort automatic generation of personalised alert thresholds for patients with copd
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AT shahsa automaticgenerationofpersonalisedalertthresholdsforpatientswithcopd
AT gibsono automaticgenerationofpersonalisedalertthresholdsforpatientswithcopd
AT rutterh automaticgenerationofpersonalisedalertthresholdsforpatientswithcopd
AT farmera automaticgenerationofpersonalisedalertthresholdsforpatientswithcopd
AT tarassenkol automaticgenerationofpersonalisedalertthresholdsforpatientswithcopd