Classification of Fall Types in Parkinson's Disease from Self-report Data Using Natural Language Processing

Abstract

Falls are a leading cause of injury globally, and people with Parkinson’s disease are particularly at risk. An important step in reducing the probability of falls is to identify their causes, but manually classifying fall types is laborious and requires expertise. Natural language processing (NLP) approaches hold potential to automate fall type identification from descriptions. The aim of this study was to develop and evaluate NLP–based methods to classify fall types from Parkinson’s disease patient self-report data. We trained supervised NLP classifiers using an existing dataset consisting of both structured and unstructured data, including the age, gender, and duration of Parkinson's disease of the faller, as well as the fall location, free-text fall description, and fall class of each fall. We trained supervised classification models to predict fall class based on these attributes, and then performed an ablation study to determine the most important factors influencing the model. The best performing classifier was a hard voting ensemble model that combined the Adaboost, unweighted decision tree, weighted k-nearest neighbor, naïve Bayes, random forest, and support vector machine classifiers. On the testing set, this ensemble classifier achieved an F₁-macro of 0.89. We also experimented with a transformer-based model, but its performance was subpar compared to that of the other models. Our study demonstrated that automatic fall type classification in Parkinson's disease patients is possible via NLP and supervised classification.