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Home monitoring wit...
Home monitoring with connected mobile devices for asthma attack prediction with machine learning
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- Tsang, Kevin C H (author)
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK; Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK
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- Pinnock, Hilary (author)
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK
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- Wilson, Andrew M (author)
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK; Norwich Medical School, University of East Anglia, Norwich, UK; Norwich University Hospital Foundation Trust, Colney Lane, Norwich, UK
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- Salvi, Dario (author)
- Malmö universitet,Internet of Things and People (IOTAP),Institutionen för datavetenskap och medieteknik (DVMT)
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- Shah, Syed Ahmar (author)
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK; Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK
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(creator_code:org_t)
- Nature Publishing Group, 2023
- 2023
- English.
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In: Scientific Data. - : Nature Publishing Group. - 2052-4463. ; 10:1
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https://doi.org/10.1...
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https://mau.diva-por... (primary) (Raw object)
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Abstract
Subject headings
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- Monitoring asthma is essential for self-management. However, traditional monitoring methods require high levels of active engagement, and some patients may find this tedious. Passive monitoring with mobile-health devices, especially when combined with machine-learning, provides an avenue to reduce management burden. Data for developing machine-learning algorithms are scarce, and gathering new data is expensive. A few datasets, such as the Asthma Mobile Health Study, are publicly available, but they only consist of self-reported diaries and lack any objective and passively collected data. To fill this gap, we carried out a 2-phase, 7-month AAMOS-00 observational study to monitor asthma using three smart-monitoring devices (smart-peak-flow-meter/smart-inhaler/smartwatch), and daily symptom questionnaires. Combined with localised weather, pollen, and air-quality reports, we collected a rich longitudinal dataset to explore the feasibility of passive monitoring and asthma attack prediction. This valuable anonymised dataset for phase-2 of the study (device monitoring) has been made publicly available. Between June-2021 and June-2022, in the midst of UK's COVID-19 lockdowns, 22 participants across the UK provided 2,054 unique patient-days of data.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Lungmedicin och allergi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Respiratory Medicine and Allergy (hsv//eng)
Publication and Content Type
- ref (subject category)
- art (subject category)
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