Abstract
© The Author(s) 2020. 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.
RAS ID
35380
Document Type
Journal Article
Date of Publication
2020
Volume
19
Funding Information
National Institutes of Health National Institute of Nursing Research Touch-mark Foundation Washington State University Lindblad Scholarship Funds
School
School of Nursing and Midwifery
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Publisher
SAGE
Recommended Citation
Fritz, R. L., & Dermody, G. (2020). Interpreting health events in big data using qualitative traditions. DOI: https://doi.org/10.1177/1609406920976453
Included in
Artificial Intelligence and Robotics Commons, Data Science Commons, Nursing Commons, Science and Technology Studies Commons, Translational Medical Research Commons
Comments
Fritz, R. L., & Dermody, G. (2020). Interpreting health events in big data using qualitative traditions. International Journal of Qualitative Methods, 19, 1609406920976453. https://doi.org/10.1177/1609406920976453