Explainable cognitive decline detection in free dialogues with a Machine Learning approach based on pre-trained Large Language Models (2411.02036v1)

Abstract: Cognitive and neurological impairments are very common, but only a small proportion of affected individuals are diagnosed and treated, partly because of the high costs associated with frequent screening. Detecting pre-illness stages and analyzing the progression of neurological disorders through effective and efficient intelligent systems can be beneficial for timely diagnosis and early intervention. We propose using LLMs to extract features from free dialogues to detect cognitive decline. These features comprise high-level reasoning content-independent features (such as comprehension, decreased awareness, increased distraction, and memory problems). Our solution comprises (i) preprocessing, (ii) feature engineering via Natural Language Processing techniques and prompt engineering, (iii) feature analysis and selection to optimize performance, and (iv) classification, supported by automatic explainability. We also explore how to improve Chatgpt's direct cognitive impairment prediction capabilities using the best features in our models. Evaluation metrics obtained endorse the effectiveness of a mixed approach combining feature extraction with Chatgpt and a specialized Machine Learning model to detect cognitive decline within free-form conversational dialogues with older adults. Ultimately, our work may facilitate the development of an inexpensive, non-invasive, and rapid means of detecting and explaining cognitive decline.

• The paper introduces a hybrid ML method using LLM-derived features for explainable cognitive decline detection from free dialogues.
• A hybrid ML model combining LLM features achieved 98.47% accuracy, far exceeding the 57.17% accuracy of the standalone LLM.
• This methodology provides a scalable, non-invasive way to automate cognitive assessment outside clinical settings, improving interpretability for healthcare applications.

An Analytical Overview of a Machine Learning Approach for Explainable Cognitive Decline Detection Using LLMs

In this paper, the authors present a comprehensive paper on utilizing LLMs for the detection of cognitive decline through analysis of free-form dialogues. The work primarily focuses on integrating LLMs with ML techniques to provide an explainable methodology for diagnosing cognitive impairment in older adults, proposing an architecture that combines the strengths of both domains.

Methodology and Implementation

The paper outlines a multi-step process that includes preprocessing, feature engineering, feature analysis, classification, and explainability. The core of the methodology involves extracting content-independent features, such as those related to reasoning and language use, from user dialogues with ChatGPT, leveraging its language processing capabilities. These features include metrics detecting memory issues, disconnection, emotional states, language register, and more, processed through prompting techniques. The extracted features are analyzed to identify the most impactful ones for training ML classifiers, such as Naive Bayes (NB), Decision Tree (DT), and Random Forest (RF), to predict cognitive decline.

Notably, the authors implement a hybrid system, which juxtaposes content-dependent text features against the content-independent linguistic parameters derived from LLMs. The performance of these models is validated on a dataset composed of transcribed dialogues obtained through an entertainment application, Celia, designed to engage elderly individuals through interactive conversations.

Results and Performance Analysis

The experiments employ $10$-fold cross-validation to ensure robustness and reliability of the model's predictive capabilities. RF exhibited superior performance with an accuracy of 98.47% in scenario 2, where the model combined content-independent features with ML classifiers. This noteworthy result underscores the efficacy of integrating LLM-derived features in enhancing model precision over traditional text-based approaches alone, marked by scenario 1's lower performance metrics.

Furthermore, when tested, ChatGPT's standalone accuracy in predicting cognitive impairment directly from dialogues measured notably at only 57.17%. Enhanced prompts using relevant features improved this figure to 61.19%, yet these results highlight the greater precision attainable from a specialized ML model equipped with LLM-extracted features.

Implications and Future Directions

The findings presented in this paper emphasize the potential for employing LLMs in healthcare, particularly in the context of intelligent dialogue systems aimed at non-invasively detecting neurological conditions. The methodology promotes an approach that is more scalable due to reduced token consumption costs and the automation of cognitive assessment outside clinical environments, fostering longitudinal research applications.

There is also a substantive discussion on the added interpretability of decisions, a critical aspect in medical applications, achieved through explainability techniques that trace the decision-making process of ML models back to human-understandable terms.

The future scope outlined by the authors suggests enhancing the model's sensitivity to varying dialogue contexts, potentially via reinforcement learning strategies, and expanding beyond foundational capabilities of LLMs. Furthermore, the exploration of LLMs’ burgeoning capabilities could lend itself to broader healthcare challenges, including personalized medicine.

Conclusion

This paper provides valuable insights into the fusion of LLMs and ML models to achieve explainable and efficient cognitive impairment detection. The proposed architecture not only heightens the accuracy of cognitive decline predictions but also aligns with the broader objectives of modern AI research: improving healthcare outcomes while maintaining comprehensibility and applicability across diverse settings. The groundwork laid here paves the way for continued exploration into similarly innovative intersections of language technology and health sciences.