Home › Research › Digital health tools
Explainable machine learning models for identifying mild cognitive impairment in older patients with chronic pain
Xiaoang Zhang, Yu‐Ping Liao, Daying Zhang, Weichen Liu, Zhijian Wang, Yaxin Jin, Shushu Chen, Jianmei Wei. BMC Nursing, 2025-01-21
Topic: Risk, prevention and care in population studies
Age, education level, number of pain sites, pain duration, pain level, depression and sleep quality were risk factors of MCI in older patients with chronic pain. The Extreme Gradient Boosting (XGBoost) model performed best (AUC 0.925), with pain level, age, and depression as the most important variables. We successfully developed 9 machine learning models to identify MCI risk. These models provide a tool for nurses to detect MCI risk early. We recommend that nurses integrate machine learning techniques into clinical nursing practice for managing MCI. However, these findings require validation with longitudinal data to confirm predictive validity for MCI progression.
Key findings
- Age, education level, number of pain sites, pain duration, pain level, depression and sleep quality were risk factors of MCI in older patients with chronic pain.
- The Extreme Gradient Boosting (XGBoost) model performed best (AUC 0.925), with pain level, age, and depression as the most important variables.
- We successfully developed 9 machine learning models to identify MCI risk.
- These models provide a tool for nurses to detect MCI risk early.
- We recommend that nurses integrate machine learning techniques into clinical nursing practice for managing MCI.
Why it matters
Nine machine learning models were developed to identify risk of mild cognitive impairment in older patients with chronic pain, with the XGBoost model performing best and pain level, age, and depression as the most important variables. The authors note these findings need validation with longitudinal data, and this is a risk-detection tool, not a treatment, cure, or prevention.
New to this subject? Start with our plain-English guide: Digital health →
Study details
- Study designlongitudinal study
- Participants612
- Effect sizeAUC 0.925
- Evidence strengthModerate
How this study scored 55/100
Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (BMC Nursing) |
| Journal's citation record | 1 / 4 | BMC Nursing: journal h-index 100 (OpenAlex, 2026-10-29) |
| Study design | 7 / 12 | A longitudinal study following the same people over time |
| Strength of evidence | 3 / 5 | Rated “moderate” 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 | 2 / 4 | 612 participants |
| Effect reported | 3 / 3 | Effect reported with a statistic (interval, p-value, ratio or similar) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 2 / 7 | Cited 10 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 3.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 3 / 6 | Cited 6.4× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 1 other studies in this library |
| Still being cited | 1 / 4 | Cited 10 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Jianmei Wei: h-index 12 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Nanchang University: institution h-index 361 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 39838390) |
| Free to read | 2 / 2 | Free to read |
| Total | 55 / 100 | — |
What each of these 20 components measures, and what this score is not →
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 8 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
Zhang, X., Liao, Y., Zhang, D., Liu, W., Wang, Z., Jin, Y., et al. (2025). Explainable machine learning models for identifying mild cognitive impairment in older patients with chronic pain. BMC Nursing. https://doi.org/10.1186/s12912-025-02723-8
Vancouver
Zhang X, Liao Y, Zhang D, Liu W, Wang Z, Jin Y, et al. Explainable machine learning models for identifying mild cognitive impairment in older patients with chronic pain. BMC Nursing. 2025. doi:10.1186/s12912-025-02723-8. PMID: 39838390.
BibTeX
@article{zhang2025-39838390,
author = {Zhang, Xiaoang and Liao, Yu‐Ping and Zhang, Daying and Liu, Weichen and Wang, Zhijian and Jin, Yaxin and Chen, Shushu and Wei, Jianmei},
title = {{Explainable machine learning models for identifying mild cognitive impairment in older patients with chronic pain}},
journal = {BMC Nursing},
year = {2025},
doi = {10.1186/s12912-025-02723-8},
pmid = {39838390},
url = {https://doi.org/10.1186/s12912-025-02723-8}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here. Showing 12 of 15.
Later studies that cite this one
1 study in the alzhub library lists this one in its references.
Related studies in Digital health tools
Also tagged “assessment”
840 studies in the library carry the topic tag “assessment”; these are this study's neighbours in a fixed, alphabetical-by-hash tour of them, not the closest in content. All 840 tagged studies.