Co-design and evaluation of an intelligent decision support system for stroke rehabilitation assessment

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...

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Main Authors: LEE, Min Hun, SIEWIOREK, Daniel P., SMAILAGIC, Asim, BERNARDINO, Alexandre, BADIA, Sergi
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Language:English
Published: Institutional Knowledge at Singapore Management University 2020
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Online Access:https://ink.library.smu.edu.sg/sis_research/6791
https://ink.library.smu.edu.sg/context/sis_research/article/7794/viewcontent/3415227.pdf
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spelling sg-smu-ink.sis_research-77942022-01-27T09:58:28Z Co-design and evaluation of an intelligent decision support system for stroke rehabilitation assessment LEE, Min Hun SIEWIOREK, Daniel P. SMAILAGIC, Asim BERNARDINO, Alexandre BADIA, Sergi 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. 2020-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6791 info:doi/10.1145/3415227 https://ink.library.smu.edu.sg/context/sis_research/article/7794/viewcontent/3415227.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Human-AI Interaction Explainable AI Machine Learning Decision Support Systems Stroke Rehabilitation Assessment Artificial Intelligence and Robotics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Human-AI Interaction
Explainable AI
Machine Learning
Decision Support Systems
Stroke Rehabilitation Assessment
Artificial Intelligence and Robotics
spellingShingle Human-AI Interaction
Explainable AI
Machine Learning
Decision Support Systems
Stroke Rehabilitation Assessment
Artificial Intelligence and Robotics
LEE, Min Hun
SIEWIOREK, Daniel P.
SMAILAGIC, Asim
BERNARDINO, Alexandre
BADIA, Sergi
Co-design and evaluation of an intelligent decision support system for stroke rehabilitation assessment
description 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.
format text
author LEE, Min Hun
SIEWIOREK, Daniel P.
SMAILAGIC, Asim
BERNARDINO, Alexandre
BADIA, Sergi
author_facet LEE, Min Hun
SIEWIOREK, Daniel P.
SMAILAGIC, Asim
BERNARDINO, Alexandre
BADIA, Sergi
author_sort LEE, Min Hun
title Co-design and evaluation of an intelligent decision support system for stroke rehabilitation assessment
title_short Co-design and evaluation of an intelligent decision support system for stroke rehabilitation assessment
title_full Co-design and evaluation of an intelligent decision support system for stroke rehabilitation assessment
title_fullStr Co-design and evaluation of an intelligent decision support system for stroke rehabilitation assessment
title_full_unstemmed Co-design and evaluation of an intelligent decision support system for stroke rehabilitation assessment
title_sort co-design and evaluation of an intelligent decision support system for stroke rehabilitation assessment
publisher Institutional Knowledge at Singapore Management University
publishDate 2020
url https://ink.library.smu.edu.sg/sis_research/6791
https://ink.library.smu.edu.sg/context/sis_research/article/7794/viewcontent/3415227.pdf
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