Automated Fidelity Assessment for Strategy Training in Inpatient Rehabilitation using Natural Language Processing

• URL: http://arxiv.org/abs/2209.06727v1
• Date: Wed, 14 Sep 2022 15:33:30 GMT
• Title: Automated Fidelity Assessment for Strategy Training in Inpatient Rehabilitation using Natural Language Processing
• Authors: Hunter Osterhoudt, Courtney E. Schneider, Haneef A Mohammad, Minmei Shih, Alexandra E. Harper, Leming Zhou, Elizabeth R Skidmore, Yanshan Wang
• Abstract summary: Strategy training is a rehabilitation approach that teaches skills to reduce disability among those with cognitive impairments following a stroke. Standardized fidelity assessment is used to measure adherence to treatment principles. We developed a rule-based NLP algorithm, a long-short term memory (LSTM) model, and a bidirectional encoder representation from transformers (BERT) model for this task.
• Score: 53.096237570992294
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Strategy training is a multidisciplinary rehabilitation approach that teaches skills to reduce disability among those with cognitive impairments following a stroke. Strategy training has been shown in randomized, controlled clinical trials to be a more feasible and efficacious intervention for promoting independence than traditional rehabilitation approaches. A standardized fidelity assessment is used to measure adherence to treatment principles by examining guided and directed verbal cues in video recordings of rehabilitation sessions. Although the fidelity assessment for detecting guided and directed verbal cues is valid and feasible for single-site studies, it can become labor intensive, time consuming, and expensive in large, multi-site pragmatic trials. To address this challenge to widespread strategy training implementation, we leveraged natural language processing (NLP) techniques to automate the strategy training fidelity assessment, i.e., to automatically identify guided and directed verbal cues from video recordings of rehabilitation sessions. We developed a rule-based NLP algorithm, a long-short term memory (LSTM) model, and a bidirectional encoder representation from transformers (BERT) model for this task. The best performance was achieved by the BERT model with a 0.8075 F1-score. The findings from this study hold widespread promise in psychology and rehabilitation intervention research and practice.

Related papers

• Reframe Your Life Story: Interactive Narrative Therapist and Innovative Moment Assessment with Large Language Models [92.93521294357058]
  Narrative therapy helps individuals transform problematic life stories into empowering alternatives.<n>Current approaches lack realism in specialized psychotherapy and fail to capture therapeutic progression over time.<n>Int (Interactive Narrative Therapist) simulates expert narrative therapists by planning therapeutic stages, guiding reflection levels, and generating contextually appropriate expert-like responses.
  arXiv  Detail & Related papers  (2025-07-27T11:52:09Z)
• Rehabilitation Exercise Quality Assessment and Feedback Generation Using Large Language Models with Prompt Engineering [1.9827390755712084]
  Exercise-based rehabilitation improves quality of life and reduces morbidity, mortality, and rehospitalization.<n>Virtual platforms enable patients to complete prescribed exercises at home, while AI algorithms analyze performance, deliver feedback, and update clinicians.<n>We propose a new method in which exercise-specific features are extracted from the skeletal joints of patients performing rehabilitation exercises and fed into pre-trained language models.
  arXiv  Detail & Related papers  (2025-05-23T22:39:10Z)
• Cog-TiPRO: Iterative Prompt Refinement with LLMs to Detect Cognitive Decline via Longitudinal Voice Assistant Commands [8.516584356273825]
  Early detection of cognitive decline is crucial for enabling interventions that can slow neurodegenerative disease progression.<n>Our pilot study investigates voice assistant systems (VAS) as non-invasive tools for detecting cognitive decline through longitudinal analysis of speech patterns in voice commands.
  arXiv  Detail & Related papers  (2025-05-22T05:40:12Z)
• Multi-Modal Self-Supervised Learning for Surgical Feedback Effectiveness Assessment [66.6041949490137]
  We propose a method that integrates information from transcribed verbal feedback and corresponding surgical video to predict feedback effectiveness. Our findings show that both transcribed feedback and surgical video are individually predictive of trainee behavior changes. Our results demonstrate the potential of multi-modal learning to advance the automated assessment of surgical feedback.
  arXiv  Detail & Related papers  (2024-11-17T00:13:00Z)
• Toward Large Language Models as a Therapeutic Tool: Comparing Prompting Techniques to Improve GPT-Delivered Problem-Solving Therapy [6.952909762512736]
  We examine the effects of prompt engineering to guide Large Language Models (LLMs) in delivering parts of a Problem-Solving Therapy session via text. We demonstrate that the models' capability to deliver protocolized therapy can be improved with the proper use of prompt engineering methods.
  arXiv  Detail & Related papers  (2024-08-27T17:25:16Z)
• Rehabilitation Exercise Quality Assessment through Supervised Contrastive Learning with Hard and Soft Negatives [2.166000001057538]
  Exercise-based rehabilitation programs have proven to be effective in enhancing the quality of life and reducing mortality and rehospitalization rates. These programs commonly prescribe a variety of exercise types, leading to a distinct challenge in rehabilitation exercise assessment datasets. This paper introduces a novel supervised contrastive learning framework with hard and soft negative samples to train a single model applicable to all exercise types.
  arXiv  Detail & Related papers  (2024-03-05T08:38:25Z)
• Automated Scoring of Clinical Patient Notes using Advanced NLP and Pseudo Labeling [2.711804338865226]
  This research introduces an approach leveraging state-of-the-art Natural Language Processing (NLP) techniques. Our methodology enhances efficiency and effectiveness, significantly reducing training time without compromising performance.
  arXiv  Detail & Related papers  (2024-01-18T05:17:18Z)
• ReIL: A Framework for Reinforced Intervention-based Imitation Learning [3.0846824529023387]
  We introduce Reinforced Intervention-based Learning (ReIL), a framework consisting of a general intervention-based learning algorithm and a multi-task imitation learning model. Experimental results from real world mobile robot navigation challenges indicate that ReIL learns rapidly from sparse supervisor corrections without suffering deterioration in performance.
  arXiv  Detail & Related papers  (2022-03-29T09:30:26Z)
• Autonomous Reinforcement Learning: Formalism and Benchmarking [106.25788536376007]
  Real-world embodied learning, such as that performed by humans and animals, is situated in a continual, non-episodic world. Common benchmark tasks in RL are episodic, with the environment resetting between trials to provide the agent with multiple attempts. This discrepancy presents a major challenge when attempting to take RL algorithms developed for episodic simulated environments and run them on real-world platforms.
  arXiv  Detail & Related papers  (2021-12-17T16:28:06Z)
• Personalized Rehabilitation Robotics based on Online Learning Control [62.6606062732021]
  We propose a novel online learning control architecture, which is able to personalize the control force at run time to each individual user. We evaluate our method in an experimental user study, where the learning controller is shown to provide personalized control, while also obtaining safe interaction forces.
  arXiv  Detail & Related papers  (2021-10-01T15:28:44Z)
• Persistent Reinforcement Learning via Subgoal Curricula [114.83989499740193]
  Value-accelerated Persistent Reinforcement Learning (VaPRL) generates a curriculum of initial states. VaPRL reduces the interventions required by three orders of magnitude compared to episodic reinforcement learning.
  arXiv  Detail & Related papers  (2021-07-27T16:39:45Z)
• 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)

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.