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Published VersionCC BY-NC V4.0, Open Access
Journal article
Interpreting Health Events in Big Data Using Qualitative Traditions
International Journal of Qualitative Methods, Vol.19, pp.1-11
2020
Abstract
The training of artificial intelligence requires integrating real-world context and mathematical computations. To achieve efficacious smart health artificial intelligence, contextual clinical knowledge serving as ground truth is required. Qualitative methods are well-suited to lend consistent and valid ground truth. In this methods article, we illustrate the use of qualitative descriptive methods for providing ground truth when training an intelligent agent to detect Restless Leg Syndrome. We show how one interdisciplinary, inter-methodological research team used both sensor-based data and the participant’s description of their experience with an episode of Restless Leg Syndrome for training the intelligent agent. We make the case for clinicians with qualitative research expertise to be included at the design table to ensure optimal efficacy of smart health artificial intelligence and a positive end-user experience.
Details
- Title
- Interpreting Health Events in Big Data Using Qualitative Traditions
- Authors
- Roschelle L Fritz (Author) - Washington State UniversityGordana Dermody (Author) - Edith Cowan University
- Publication details
- International Journal of Qualitative Methods, Vol.19, pp.1-11
- Publisher
- Sage Publications Ltd.
- Date published
- 2020
- DOI
- 10.1177/1609406920976453
- ISSN
- 1609-4069
- Copyright note
- This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
- Organisation Unit
- School of Health - Nursing; University of the Sunshine Coast, Queensland; School of Nursing, Midwifery and Paramedicine - Legacy
- Language
- English
- Record Identifier
- 99511306002621
- Output Type
- Journal article
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- Social Sciences, Interdisciplinary
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