ArticleBMC nursing2025
Explainable machine learning models for identifying mild cognitive impairment in older patients with chronic pain.
Article in BMC nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Development and internal validation of an explainable machine learning model for predicting functional disability trajectories in hospitalized older patients with chronic low back pain.BMC geriatrics · 2026Article
- Predictive analysis of health-related quality of life trajectories in older patients with chronic pain based on explainable machine learning models.Health and quality of life outcomes · 2026Article
- Machine learning-based risk classification of depressive symptoms among patients with hearing loss: evidence from the Health and Retirement Study (HRS).Comprehensive psychoneuroendocrinology · 2026Article
- Predicting sleep disorder trajectories in older patients with chronic pain: an explainable machine learning approach.BMC psychiatry · 2026Article
- Development of machine learning-based predictive models for fertility intentions in patients with Crohn's disease.Frontiers in reproductive health · 2026Article
- Development of a neural network-based risk prediction model for mild cognitive impairment in older adults with functional disability.BMC public health · 2025Article
- Identifying the key influencing factors of psychological birth trauma in primiparous women with interpretable machine learning.International journal of nursing sciences · 2025Article
- Associations of social isolation with memory and cognitive function in middle-aged and older Chinese adults.Aging clinical and experimental research · 2025Article
- Machine learning-based early screening of mild cognitive impairment using nutrition-related biomarkers and functional indicators.Frontiers in aging neuroscience · 2025Article
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8 authors.
Funding
Abstract
backgroundMild cognitive impairment (MCI) is prevalent in older adults with chronic pain, making early detection crucial for dementia prevention and healthy aging. This study aimed to determine MCI risk factors in older patients with chronic pain and to develop 9 machine learning models to identify MCI risk.
methodsA total of 612 older patients with chronic pain were recruited between October 2023 and July 2024. Data collected included patients' general information, cognitive function, pain level, depression, and sleep quality. The dataset was randomly divided into training set and testing set, and processed by Min-Max Normalization and SMOTETomek comprehensive sampling. SVM-RFE and LASSO regression were used for variable selection. We then developed machine learning models and interpreted them by SHAP.
resultsAge, 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.
conclusionsWe 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.
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