Semantic Technologies for Clinically Relevant Personal Health Applications

Author
Chen, Ching-Hau; Gruen, Daniel; Harris, Jonathan; Hendler, James A.; McGuinness, Deborah L.; Monti, Marco; Rastogi, Nidhi; Seneviratne, Oshani; Zaki, Mohammed J
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2022-11-23
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Attribution-NonCommercial-NoDerivs 3.0 United States
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Chen, CH. et al. (2022). Semantic Technologies for Clinically Relevant Personal Health Applications. In: Hsueh, PY.S., Wetter, T., Zhu, X. (eds) Personal Health Informatics. Cognitive Informatics in Biomedicine and Healthcare. Springer, Cham. https://doi.org/10.1007/978-3-031-07696-1_10
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https://hdl.handle.net/20.500.13015/6323; https://doi.org/10.1007/978-3-031-07696-1_10; https://link.springer.com/chapter/10.1007/978-3-031-07696-1_10
Abstract
Despite recent advances in digital health solutions and machine learning, personal health applications that aim to modify health behaviors are still limited in their ability to offer more personalized decision support. Moreover, while many personal health applications cater to general health and well-being, there remains a significant opportunity to increase the clinical relevance of the insights being generated. This chapter describes the motivation for, and illustrative applications of, semantic technologies for enabling clinically relevant personal health applications. We present two use cases that demonstrate how semantic web technologies, in combination with machine learning and data mining methods, can be used to provide personalized insights to support behaviors that are consistent with nutritional guidelines for people with diabetes.;
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Attribution-NonCommercial-NoDerivs 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States