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.

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