Leveraging large language models through natural language processing to provide interpretable machine learning predictions of mental deterioration in real time
Open accessAbstract
Abstract Based on official estimates, 50 million people worldwide are affected by dementia, and this number increases by 10 million new patients every year. Without a cure, clinical prognostication and early intervention represent the most effective ways to delay its progression. To this end, artificial intelligence and computational linguistics can be exploited for natural language analysis, personalized assessment, monitoring, and treatment. However, traditional approaches need more semantic knowledge management and explicability capabilities. Moreover, using large language models ( llm s) for cognitive decline diagnosis is still scarce, even though these models represent the most advanced way for clinical–patient communication using intelligent systems. Consequently, we leverage an llm using the latest natural language processing ( nlp ) techniques in a chatbot solution to provide interpretable machine learning prediction of cognitive decline in real-time. Linguistic-conceptual features are exploited for appropriate natural language analysis. Through explainability, we aim to fight potential biases of the models and improve their potential to help clinical workers in their diagnosis decisions. More in detail, the proposed pipeline is composed of (i) data extraction employing nlp -based prompt engineering; (ii) stream-based data processing including feature engineering, analysis, and selection; (iii) real-time classification; and (iv) the explainability dashboard to provide visual and natural language descriptions of the prediction outcome. Classification results exceed 80% in all evaluation metrics, with a recall value for the mental deterioration class about 85%. To sum up, we contribute with an affordable, flexible, non-invasive, personalized diagnostic system to this work.
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Of the 69 reviewed studies, only three included any user-facing evaluation of explanation presentation.
“Of the 69 reviewed studies, only three [75,76,104] included any user-facing evaluation of explanation presentation.”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [76]
Sections 3.4 (p. 7) and 4.5 (p. 12) describe an explanation dashboard intended for end users, but do not report users evaluating how its explanations are presented. A search for user studies, surveys, and assessments of explanation clarity found only descriptions of the dashboard and evaluations of prediction performance. This source also cannot establish the citing review’s count of three out of 69 studies. (LLM, conf=0.70)
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- Authors
- de Arriba-Pérez, F., & García-Méndez, S.
- Affiliations
- Universidade de Vigo (ES)
- Corresponding author
- García-Méndez, Silvia
- Venue
- Arabian Journal for Science and Engineering
- Published
- 2024-08-27
- Volume
- 50
- Issue
- 15
- Pages
- 11577-11591
- ISSN
- 1319-8025 (ISSN-L)
- 2193-567X
- 2191-4281
- OpenAlex
- W4401900598
- Semantic Scholar
- 1075a9b84e62cb333a18a93fe4750d2a7cdfb190
- Cited by
- 8
- References
- 52
- Citations by year
- 2026: 3 · 2025: 5
- FWCI
- 2.46
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Arabian Journal for Science and Engineering (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- Investigo Institutional repository of UVigo (Universidade de Vigo) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- Xunta de Galicia (ED481B-2021-118, ED481B-2022-093)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- dementia, cognitive decline prediction, large language models, chatbots, model interpretability, explainability, prompt engineering, real-time classification, clinical decision support, natural language processing
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