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Democratizing Digital Health Algorithms: RESTful Machine Learning Web Services

  • 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.

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Metadaten
Author:Lucas Weber, Ralf SeepoldORCiDGND, Natividad Martínez MadridORCiD
DOI:https://doi.org/10.1007/978-3-031-16855-0_2
ISBN:978-3-031-16855-0
ISBN:978-3-031-16854-3
Parent Title (English):Social Innovation in Long-Term Care Through Digitalization : Proceedings of the German-Italian Workshop LTC-2021 (WS-LTC 2021)
Publisher:Springer International Publishing
Place of publication:Cham
Document Type:Conference Proceeding
Language:English
Year of Publication:2022
Release Date:2022/10/05
Tag:Long-term care; Machine learning; RESTful API
First Page:7
Last Page:15
Note:
Corresponding author: Lucas Weber
Institutes:Institut für Angewandte Forschung - IAF
Relevance:Keine peer reviewed Publikation (Wissenschaftlicher Artikel und Aufsatz, Proceeding, Artikel in Tagungsband)
Open Access?:Nein
Licence (German):License LogoUrheberrechtlich geschützt