@inproceedings{6eb2e7ec4d1f4c9881629334e79d749a,
title = "Automatic Generation of Personalised Alert Thresholds for Patients with COPD",
abstract = "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.",
keywords = "m-Health, novelty detection, COPD, chronic diseases, digital health, NOVELTY DETECTION",
author = "Carmelo Velardo and Shah, {Syed Ahmar} and Oliver Gibson and Heather Rutter and Andrew Farmer and Lionel Tarassenko",
year = "2014",
month = nov,
day = "13",
language = "English",
series = "European Signal Processing Conference",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "1990--1994",
booktitle = "2014 PROCEEDINGS OF THE 22ND EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO)",
address = "United States",
note = "22nd European Signal Processing Conference (EUSIPCO) ; Conference date: 01-09-2014 Through 05-09-2014",
}