Opportunities of a Machine Learning-based Decision Support System for Stroke Rehabilitation Assessment

• URL: http://arxiv.org/abs/2002.12261v2
• Date: Mon, 2 Mar 2020 17:22:42 GMT
• Title: Opportunities of a Machine Learning-based Decision Support System for Stroke Rehabilitation Assessment
• Authors: Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, and Sergi Berm\'udez i Badia
• Abstract summary: Rehabilitation assessment is critical to determine an adequate intervention for a patient. Current practices of assessment mainly rely on therapist's experience, and assessment is infrequently executed due to the limited availability of a therapist. We developed an intelligent decision support system that can identify salient features of assessment using reinforcement learning.
• Score: 64.52563354823711
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rehabilitation assessment is critical to determine an adequate intervention for a patient. However, the current practices of assessment mainly rely on therapist's experience, and assessment is infrequently executed due to the limited availability of a therapist. In this paper, we identified the needs of therapists to assess patient's functional abilities (e.g. alternative perspective on assessment with quantitative information on patient's exercise motions). As a result, we developed an intelligent decision support system that can identify salient features of assessment using reinforcement learning to assess the quality of motion and summarize patient specific analysis. We evaluated this system with seven therapists using the dataset from 15 patient performing three exercises. The evaluation demonstrates that our system is preferred over a traditional system without analysis while presenting more useful information and significantly increasing the agreement over therapists' evaluation from 0.6600 to 0.7108 F1-scores ($p <0.05$). We discuss the importance of presenting contextually relevant and salient information and adaptation to develop a human and machine collaborative decision making system.

Related papers

• Multi-Session Client-Centered Treatment Outcome Evaluation in Psychotherapy [9.299504332783325]
  IPAEval is a client-Informed Psychological Assessment-based Evaluation framework. It automates treatment outcome evaluations from the client's perspective using clinical interviews. IPAEval effectively tracks symptom severity and treatment outcomes over multiple sessions, outperforming previous single-session models.
  arXiv  Detail & Related papers  (2024-10-08T08:54:38Z)
• Design, Development, and Evaluation of an Interactive Personalized Social Robot to Monitor and Coach Post-Stroke Rehabilitation Exercises [68.37238218842089]
  We develop an interactive social robot exercise coaching system for personalized rehabilitation. This system integrates a neural network model with a rule-based model to automatically monitor and assess patients' rehabilitation exercises. Our system can adapt to new participants and achieved 0.81 average performance to assess their exercises, which is comparable to the experts' agreement level.
  arXiv  Detail & Related papers  (2023-05-12T17:37:04Z)
• SupervisorBot: NLP-Annotated Real-Time Recommendations of Psychotherapy Treatment Strategies with Deep Reinforcement Learning [13.173307471333619]
  We propose a recommendation system that suggests treatment strategies to a therapist during the psychotherapy session in real-time. Our system uses a turn-level rating mechanism that predicts the therapeutic outcome by computing a similarity score between the deep embedding of a scoring inventory, and the current sentence that the patient is speaking.
  arXiv  Detail & Related papers  (2022-08-27T19:22:53Z)
• Exploring Runtime Decision Support for Trauma Resuscitation [7.3268099910347715]
  We develop a treatment recommender system to provide runtime next-minute activity predictions. The system uses patient context (e.g., demographics and vital signs) and process context (e.g., activities) to continuously predict activities that will be performed in the next minute. The best model achieved an average F1-score of 0.67 for 61 activity types.
  arXiv  Detail & Related papers  (2022-07-06T19:02:43Z)
• Optimal discharge of patients from intensive care via a data-driven policy learning framework [58.720142291102135]
  It is important that the patient discharge task addresses the nuanced trade-off between decreasing a patient's length of stay and the risk of readmission or even death following the discharge decision. This work introduces an end-to-end general framework for capturing this trade-off to recommend optimal discharge timing decisions. A data-driven approach is used to derive a parsimonious, discrete state space representation that captures a patient's physiological condition.
  arXiv  Detail & Related papers  (2021-12-17T04:39:33Z)
• "Am I A Good Therapist?" Automated Evaluation Of Psychotherapy Skills Using Speech And Language Technologies [38.726068038788384]
  We describe our platform and its performance, using a dataset of more than 5,000 recordings. Our system gives comprehensive feedback to the therapist, including information about the dynamics of the session. We are confident that a widespread use of automated psychotherapy rating tools in the near future will augment experts' capabilities.
  arXiv  Detail & Related papers  (2021-02-22T18:52:52Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)
• A Review of Computational Approaches for Evaluation of Rehabilitation Exercises [58.720142291102135]
  This paper reviews computational approaches for evaluating patient performance in rehabilitation programs using motion capture systems. The reviewed computational methods for exercise evaluation are grouped into three main categories: discrete movement score, rule-based, and template-based approaches.
  arXiv  Detail & Related papers  (2020-02-29T22:18:56Z)
• Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions [48.91284724066349]
  Off-policy evaluation in reinforcement learning offers the chance of using observational data to improve future outcomes in domains such as healthcare and education. Traditional measures such as confidence intervals may be insufficient due to noise, limited data and confounding. We develop a method that could serve as a hybrid human-AI system, to enable human experts to analyze the validity of policy evaluation estimates.
  arXiv  Detail & Related papers  (2020-02-10T00:26:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.