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Machine Learning Prediction of Agitation in Dementia Patients Using Sleep and Physiological Data
Keshav Ramesh, Anna Yakoub, Youssef Ghoneim, Rehab Al Korabi, Jayroop Ramesh, Assim A. Sagahyroon, Fadi Aloul. Applied Sciences, 2025-09-10
Topic: Risk, prevention and care in population studies
Dementia is a progressive condition that affects cognitive and functional abilities. Psycho-motor agitation represents a frequent and challenging manifestation in People Living with Dementia (PLwD). This behavior contributes to heightened distress and increased risk of harm for patients, while posing a significant burden for caregivers, who must navigate the complexities of managing unpredictable and potentially harmful agitation episodes. Accurately predicting and promptly responding to agitation events is thus critical for enhancing the safety and well-being of PLwD. Leveraging artificial intelligence, tools can be used to monitor behavioral patterns and alert healthcare providers about potential agitation to facilitate timely and effective interventions. Despite the link between poor sleep quality and the likelihood of agitation, there remains a gap in utilizing sleep parameters for predictive analytics in this domain. This study explores the potential of integrating sleep and associated physiological data to predict the risk of agitation in dementia patients the next day, leveraging the Technology Integrated Health Management (TIHM) dataset. Our analysis reveals that the LightGBM model, enhanced with combined feature sets, delivers superior performance, achieving a weighted F1 score of 93.6% compared to standard baseline models. The findings underscore the value of incorporating sleep data into automated models and advocate for continued efforts to develop long-term agitation prediction methods.
Key findings
- This behavior contributes to heightened distress and increased risk of harm for patients, while posing a significant burden for caregivers, who must navigate the complexities of managing unpredictable and potentially harmful agitation episodes.
- Our analysis reveals that the LightGBM model, enhanced with combined feature sets, delivers superior performance, achieving a weighted F1 score of 93.6% compared to standard baseline models.
Why it matters
This study uses sleep and physiological data to predict next-day agitation in people with dementia, which could help caregivers respond sooner. It addresses symptom management only and does not test any treatment, cure or prevention of Alzheimer's disease.
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Study details
- Effect size93.6%
- Evidence strengthLow
How this study scored 33/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Applied Sciences) |
| Journal's citation record | 2 / 4 | Applied Sciences: journal h-index 230 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 1 / 7 | Cited 1 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 1 / 6 | Cited 0.6× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 1 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Fadi Aloul: h-index 34 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | American University of Sharjah: institution h-index 231 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Total | 33 / 100 | — |
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Read the original
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 7 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
APA 7th
Ramesh, K., Yakoub, A., Ghoneim, Y., Korabi, R. A., Ramesh, J., Sagahyroon, A. A., et al. (2025). Machine Learning Prediction of Agitation in Dementia Patients Using Sleep and Physiological Data. Applied Sciences. https://doi.org/10.3390/app15189908
Vancouver
Ramesh K, Yakoub A, Ghoneim Y, Korabi RA, Ramesh J, Sagahyroon AA, et al. Machine Learning Prediction of Agitation in Dementia Patients Using Sleep and Physiological Data. Applied Sciences. 2025. doi:10.3390/app15189908.
BibTeX
@article{ramesh2025-0b056e8f,
author = {Ramesh, Keshav and Yakoub, Anna and Ghoneim, Youssef and Korabi, Rehab Al and Ramesh, Jayroop and Sagahyroon, Assim A. and Aloul, Fadi},
title = {{Machine Learning Prediction of Agitation in Dementia Patients Using Sleep and Physiological Data}},
journal = {Applied Sciences},
year = {2025},
doi = {10.3390/app15189908},
url = {https://doi.org/10.3390/app15189908}
}
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