TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Weber, Lucas
A1  - Seepold, Ralf
A1  - Martínez Madrid, Natividad
ED  - Conti, Massimo
ED  - Orcioni, Simone
T1  - Democratizing digital health algorithms: RESTful machine learning web services
T2  - Social innovation in long-term care through digitalization : proceedings of the German-Italian Workshop LTC-2021, Ancona, Italy, 2-4 November 2021 (Lecture notes in bioengineering)
N2  - There have been substantial research efforts for algorithms to improve continuous and automated assessment of various health-related questions in recent years. This paper addresses the deployment gap between those improving algorithms and their usability in care and mobile health applications. In practice, most algorithms require significant and founded technical knowledge to be deployed at home or support healthcare professionals. Therefore, the digital participation of persons in need of health care professionals lacks a usable interface to use the current technological advances. In this paper, we propose applying algorithms taken from research as web-based microservices following the common approach of a RESTful service to bridge the gap and make algorithms accessible to caregivers and patients without technical knowledge and extended hardware capabilities. We address implementation details, interpretation and realization of guidelines, and privacy concerns using our self-implemented example. Also, we address further usability guidelines and our approach to those.
KW  - availability
KW  - long-term care
KW  - machine learning
KW  - RESTful API
Y1  - 2022
SN  - 978-3-031-16854-3
SB  - 978-3-031-16854-3
SN  - 978-3-031-16855-0
SB  - 978-3-031-16855-0
U6  - https://doi.org/10.1007/978-3-031-16855-0_2
DO  - https://doi.org/10.1007/978-3-031-16855-0_2
SP  - 7
EP  - 15
S1  - 9
PB  - Springer
CY  - Cham
ER  -