A Machine Learning Classification of Individuals with Mild Cognitive Impairment into Variants from Writing

Abstract

Introduction: Individuals with Mild Cognitive Impairment (MCI), a transitional stage between cognitively healthy aging and dementia, are characterized by subtle neurocognitive changes. Clinically, they can be grouped into two main variants, namely into patients with amnestic MCI (aMCI) and non-amnestic MCI (naMCI). The distinction of the two variants is known to be clinically significant as they exhibit different progression rates to dementia. However, it has been particularly challenging to classify the two variants robustly. Recent research indicates that linguistic changes may manifest as one of the early indicators of pathology. Therefore, we focused on MCI’s discourse-level writing samples in this study. We hypothesized that a written picture description task can provide information that can be used as an ecological, cost-effective classification system between the two variants. Methods: We included one hundred sixty-nine individuals diagnosed with either aMCI or naMCI who received neurophysiological evaluations in addition to a short-written picture description task. Natural Language Processing (NLP) and BERT pre-trained Language Models were utilized to analyze the writing samples. Results: We showed that the written picture description task provided 90% overall classification accuracy for the best classification models, which performs better than cognitive measures. Discussion: Written discourses analyzed the AI models can automatically assess individuals with aMCI and naMCI and facilitate diagnosis, prognosis, therapy planning, and evaluation.