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Publication
Co-design and evaluation of an intelligent decision support system for stroke rehabilitation assessment
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Advisor(s)
Abstract(s)
Clinical decision support systems have the potential to improve work flows of experts in practice (e.g. therapist’s
evidence-based rehabilitation assessment). However, the adoption of these systems is challenging, and the
gains of these systems have not fully demonstrated yet. In this paper, we identified the needs of therapists to
assess patient’s functional abilities (e.g. alternative perspectives with quantitative information on patient’s
exercise motions). As a result, we co-designed and developed an intelligent decision support system that
automatically identifies salient features of assessment using reinforcement learning to assess the quality
of motion and generate patient-specific analysis. We evaluated this system with seven therapists using the
dataset from 15 patients performing three exercises. The results show that therapists have higher usage intent
on our system than a traditional system without patient-specific analysis (𝑝 < 0.05). While presenting richer
information (𝑝 < 0.10), our system significantly reduces therapists’ effort on assessment (𝑝 < 0.10) and
improves their agreement on assessment from 0.66 to 0.71 F1-scores (𝑝 < 0.01). This work discusses the
importance of human centered design and development of a machine learning-based decision support system
that presents contextually relevant information and salient explanations on its prediction for better adoption
in practice.
Description
Keywords
Human-centered computing Interactive systems and tools User studies Applied computing Health care information systems Computing methodologies Reinforcement learning . Faculdade de Ciências Exatas e da Engenharia
Citation
Lee, M. H., Siewiorek, D. P., Smailagic, A., Bernardino, A., & Bermúdez i Badia, S. (2020). Co-design and evaluation of an intelligent decision support system for stroke rehabilitation assessment. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW2), 1-27.
Publisher
ACM