A human-AI collaborative approach for clinical decision making on rehabilitation assessment
Publication Type
Conference Proceeding Article
Publication Date
5-2021
Abstract
Advances in artificial intelligence (AI) have made it increasingly applicable to supplement expert’s decision-making in the form of a decision support system on various tasks. For instance, an AI-based system can provide therapists quantitative analysis on patient’s status to improve practices of rehabilitation assessment. However, there is limited knowledge on the potential of these systems. In this paper, we present the development and evaluation of an interactive AI-based system that supports collaborative decision making with therapists for rehabilitation assessment. This system automatically identifies salient features of assessment to generate patient-specific analysis for therapists, and tunes with their feedback. In two evaluations with therapists, we found that our system supports therapists significantly higher agreement on assessment (0.71 average F1-score) than a traditional system without analysis (0.66 average F1-score, p
Keywords
Decision support systems, Explainable and interactive machine learning, Human-ai interaction/collaboration, Personalization, Stroke rehabilitation assessment
Discipline
Artificial Intelligence and Robotics
Research Areas
Data Science and Engineering
Publication
Proceedings of the ACM Conference on Human Factors in Computing Systems. CHI ’21
First Page
1
Last Page
14
ISBN
9781450380966
Identifier
10.1145/3411764.3445472
Publisher
Association for Computing Machinery
City or Country
Virtual, Online
Citation
LEE, Min Hun; SIEWIOREK, Daniel P.; SMAILAGIC, Asim; BERNARDINO, Alexandre; and BADIA, Sergi Bermúdez i.
A human-AI collaborative approach for clinical decision making on rehabilitation assessment. (2021). Proceedings of the ACM Conference on Human Factors in Computing Systems. CHI ’21. 1-14.
Available at: https://ink.library.smu.edu.sg/sis_research/6690
Additional URL
http://doi.org/10.1145/3411764.3445472