Semantic Coherence Markers for the Early Diagnosis of the Alzheimer Disease

Abstract: In this work we explore how LLMs can be employed to analyze language and discriminate between mentally impaired and healthy subjects through the perplexity metric. Perplexity was originally conceived as an information-theoretic measure to assess how much a given LLM is suited to predict a text sequence or, equivalently, how much a word sequence fits into a specific LLM. We carried out an extensive experimentation with the publicly available data, and employed LLMs as diverse as N-grams, from 2-grams to 5-grams, and GPT-2, a transformer-based LLM. We investigated whether perplexity scores may be used to discriminate between the transcripts of healthy subjects and subjects suffering from Alzheimer Disease (AD). Our best performing models achieved full accuracy and F-score (1.00 in both precision/specificity and recall/sensitivity) in categorizing subjects from both the AD class and control subjects. These results suggest that perplexity can be a valuable analytical metrics with potential application to supporting early diagnosis of symptoms of mental disorders.