A human-ai collaborative approach for clinical decision making on rehabilitation assessment

Lee, Min Hun; Siewiorek, Daniel P. P.; Smailagic, Asim; Bernardino, Alexandre; Bermúdez i Badia, Sergi

http://hdl.handle.net/10400.13/4444

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Authors

Lee, Min Hun

Siewiorek, Daniel P. P.

Smailagic, Asim

Bernardino, Alexandre

Bermúdez i Badia, Sergi

Abstract(s)

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 evalu ations with therapists, we found that our system supports thera pists significantly higher agreement on assessment (0.71 average F1-score) than a traditional system without analysis (0.66 average F1-score, p < 0.05). After tuning with therapist’s feedback, our sys tem significantly improves its performance from 0.8377 to 0.9116 average F1-scores (p < 0.01). This work discusses the potential of a human-AI collaborative system to support more accurate decision making while learning from each other’s strengths

Keywords

Human-AI interaction/collaboration Decision support systems Explainable and interactive machine learning Personalization Stroke rehabilitation assessment . Faculdade de Ciências Exatas e da Engenharia

URI

http://hdl.handle.net/10400.13/4444

Citation

ee, M. H., Siewiorek, D. P., Smailagic, A., Bernardino, A., & Bermúdez i Badia, S. (2021, May). A human-ai collaborative approach for clinical decision making on rehabilitation assessment. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-14).