# Automatic detection of cognitive impairment in elderly people using an entertainment chatbot with Natural Language Processing capabilities

arXiv: https://arxiv.org/abs/2405.18542 · HTML: https://mlsanity.com/paper/2405.18542/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 91 references that are in the map.
- First posted on arXiv 28 May 2024.

## Builds on (most cited first, 6 of 6)

- [A Network-based End-to-End Trainable Task-oriented Dialogue System](https://mlsanity.com/paper/1604.04562/) (2016)
- [Neural Responding Machine for Short-Text Conversation](https://mlsanity.com/paper/1503.02364/) (2015)
- [From Eliza to XiaoIce: Challenges and Opportunities with Social Chatbots](https://mlsanity.com/paper/1801.01957/) (2018)
- [Syntax-based Deep Matching of Short Texts](https://mlsanity.com/paper/1503.02427/) (2015)
- [A Pilot Study on Using an Intelligent Life-like Robot as a Companion for Elderly Individuals with Dementia and Depression](https://mlsanity.com/paper/1712.02881/) (2017)
- [A Library for Automatic Natural Language Generation of Spanish Texts](https://mlsanity.com/paper/2405.17280/) (2024)

## Built on by (most cited first, 3 of 3)

- [Explainable cognitive decline detection in free dialogues with a Machine Learning approach based on pre-trained Large Language Models](https://mlsanity.com/paper/2411.02036/) (2024)
- [Leveraging Large Language Models through Natural Language Processing to provide interpretable Machine Learning predictions of mental deterioration in real time](https://mlsanity.com/paper/2409.03375/) (2024)
- [Designing and Evaluating a Conversational Agent for Early Diagnosis of Alzheimer's Disease and Related Dementias](https://mlsanity.com/paper/2509.11478/) (2025)

## Abstract (arXiv)

Previous researchers have proposed intelligent systems for therapeutic monitoring of cognitive impairments. However, most existing practical approaches for this purpose are based on manual tests. This raises issues such as excessive caretaking effort and the white-coat effect. To avoid these issues, we present an intelligent conversational system for entertaining elderly people with news of their interest that monitors cognitive impairment transparently. Automatic chatbot dialogue stages allow assessing content description skills and detecting cognitive impairment with Machine Learning algorithms. We create these dialogue flows automatically from updated news items using Natural Language Generation techniques. The system also infers the gold standard of the answers to the questions, so it can assess cognitive capabilities automatically by comparing these answers with the user responses. It employs a similarity metric with values in [0, 1], in increasing level of similarity. To evaluate the performance and usability of our approach, we have conducted field tests with a test group of 30 elderly people in the earliest stages of dementia, under the supervision of gerontologists. In the experiments, we have analysed the effect of stress and concentration in these users. Those without cognitive impairment performed up to five times better. In particular, the similarity metric varied between 0.03, for stressed and unfocused participants, and 0.36, for relaxed and focused users. Finally, we developed a Machine Learning algorithm based on textual analysis features for automatic cognitive impairment detection, which attained accuracy, F-measure and recall levels above 80%. We have thus validated the automatic approach to detect cognitive impairment in elderly people based on entertainment content.