| Περίληψη: | In this work we exploit the openEHR framework for the representation of frailty in
ageing population in order to attain semantic interoperability, and we present the methodology
for adoption or development of archetypes. Frailty is a common clinical syndrome
in ageing population that carries an increased risk for adverse health outcomes including
falls, hospitalization, disability, and mortality. As these outcomes a ect the health and
social care planning, during the last years there is a tendency of investing in monitoring
and preventing strategies. Although a number of electronic health record (EHR) systems
have been developed, including personalized virtual patient models, there are limited ageing
population oriented systems. We also propose a framework for a one-to-one mapping
between openEHR archetypes and a column-family NoSQL database aiming at the integration
of existing and newly developed archetypes into our HBase storage system. As a
result a detailed personalized virtual patient model (VPM) of the health pro le has been
designed and constructed by unobtrusively monitoring the older person's everyday life
through a variety of embedded and wireless smart indoors and outdoors sensors, as well
as social interaction, clinical assessment and self-evaluation tests.
Focusing on the aging population as a case study, in this study we also present a
methodology for extracting and analyzing multi-sensor measurements and use them to
identify data clusters that might be linked to clinical pro les. This was achieved by fusing
information of the VPM from multiple devices, such as a wearable device for physiological
and kinetic monitoring, a dynamometer for strength measurement, a game platform and
a global positioning system (GPS) for outdoor monitoring. Principal component analysis
(PCA) was applied to remove correlations in the extracted features, followed by locally
linear embedding (LLE) to embed the data in a lower dimensional space where unsupervised
clustering is feasible. Exploration of the identi ed clusters revealed patterns of frailty
manifestation that were in high accordance with geriatric indices from several domains
(medical, cognitive, psychological, etc). Our results highlight the potential of the applied
data mining methodology for the extraction of novel frailty indicators.
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