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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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 |
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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 |
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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. |
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LEE, Min Hun SIEWIOREK, Daniel P. SMAILAGIC, Asim BERNARDINO, Alexandre BADIA, Sergi |
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LEE, Min Hun SIEWIOREK, Daniel P. SMAILAGIC, Asim BERNARDINO, Alexandre BADIA, Sergi |
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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 |
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Co-design and evaluation of an intelligent decision support system for stroke rehabilitation assessment |
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co-design and evaluation of an intelligent decision support system for stroke rehabilitation assessment |
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Institutional Knowledge at Singapore Management University |
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2020 |
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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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