Development of Classifier of Engagement in Occupation With Machine Learning (CEOML) for Quantifying Context

Abstract

In the evaluation of engagement in occupation, it is important to access the qualitative data, including the client context. However, there are no tools capable of quantifying such data. The purpose of this study is to develop a Classifier of Engagement in Occupation with Machine Learning (CEOML) that is capable of quantifying context and evaluating engagement in occupation through the application of Natural Language Processing (NLP) and to validate the model performance. A supervised machine learning approach was adopted in this study for the development of clinical artificial intelligence, and it was conducted based on the Minimum Information about Clinical Artificial Intelligence Modeling. The research object was the Twitter data comprising 1,542 tweets posted over a one-week period, beginning on April 1, 2020. Bidirectional Encoder Representations from Transformers, an NLP model, was fine-tuned to learn a dataset labeled for the status of engagement in occupation. The model performance was validated using indicators (sensitivity, specificity, positive predictive value, negative predictive value, F-measure, and area under the curve of receiver operating characteristic curve), Cohen’s weighted kappa coefficient, and the attention level of the model to the text. The CEOML demonstrated suitable model performance, on par with the Canadian Occupational Performance Measure. High interpretability of the CEOML was also confirmed based on its level of attention. The developed CEOML can quantify and classify problems of engagement in occupation based on the client context.