Digital Healthcare and Medtech News
10 November, 2022
Prediction of Exacerbation Events using myCOPD data.
As Pilot 4, my mhealth and the University of Southampton collaborated to develop a machine learning model to predict and manage worsening of chronic obstructive pulmonary disease (COPD), termed exacerbation. Results demonstrated that self-reported COPD data, using a digital health app, can be used to identify users at risk of exacerbation within 3 days with moderate discriminative ability
Prediction of Chronic Obstructive Pulmonary Disease Exacerbation Events by Using Patient Self-reported Data in a Digital Health App: Statistical Evaluation and Machine Learning Approach.
BigMedilytics (Big Data for Medical Analytics) was the largest EU-funded initiative to transform the region’s healthcare sector. Its aim was to enhance patient outcomes and increase productivity in the health sector by applying big data technologies to complex datasets while ensuring security and privacy of personal data.
Twelve pilots across eight countries, more than 11 million healthcare records, and data from other sectors including insurance and public bodies addressed three themes: Population Health and Chronic Disease Management, Oncology and Industrialization of Healthcare Services; and covered the entire Healthcare Continuum from Prevention to Diagnosis, Treatment and Home Care.
As Pilot 4, my mhealth and the University of Southampton collaborated to develop a machine learning model to predict and manage worsening of chronic obstructive pulmonary disease (COPD), termed exacerbation.
Using the cloud-based digital health application (app) myCOPD, analysis explored several in-app reported variables including medication usage, COPD assessment test scores, and symptom scores. This led to the development of both heuristic and machine learning models.
Results demonstrated that self-reported COPD data, using a digital health app, can be used to identify users at risk of exacerbation within 3 days with moderate discriminative ability (AUROC 0.727, 95% CI 0.720-0.735). Further research utilizing additional linked data (particularly from medical devices such as smart inhalers, physiological monitoring sensors, and environmental sensors) are expected to increase the accuracy of these models.
Data self-reported to health care apps designed to remotely monitor patients with COPD can be used to predict acute exacerbation events with moderate performance. This could increase personalization of care by allowing pre-emptive action to be taken to mitigate the risk of future exacerbation events.
With the Health and Social Care Secretary setting a target for 4 million to benefit from personalised care by March 2024, leveraging the data collected by these apps in prognostic models could provide increased personalization of care by allowing pre-emptive action to be taken to mitigate the risk of future exacerbation events
Findings have been published in the JMIR Medical Informatics:https://medinform.jmir.org/2022/3/e26499/
©Francis P Chmiel, Dan K Burns, John Brian Pickering, Alison Blythin, Thomas MA Wilkinson, Michael J Boniface. Originally published in JMIR Medical Informatics (https://medinform.jmir.org/2022/3/e26499), 21.03.2022.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication onhttps://medinform.jmir.org/, as well as this copyright and license information must be included.
About my mhealth
my mhealth’s digital therapeutics have been prescribed to over 90,000 patients with chronic conditions, resulting in reduced morbidity and hospital admissions. It serves patients across a range of long-term conditions, including COPD, asthma, diabetes and cardiovascular disease. Real world and clinical trial evidence demonstrates the efficacy of digital interventions on the my mhealth platform. For more information on my mhealth, visit www.mymhealth.com.
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