ArticleScientific reports2023
Machine learning-based predictive models for the occurrence of behavioral and psychological symptoms of dementia: model development and validation.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 3 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- AI and Wearables for Early Detection of Cognitive Impairment and Dementia: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Advancing Remote Monitoring for Patients With Alzheimer Disease and Related Dementias: Systematic Review.JMIR aging · 2025Pooled it
- Activity and Behavioral Recognition Using Sensing Technology in Persons with Parkinson's Disease or Dementia: An Umbrella Review of the Literature.Sensors (Basel, Switzerland) · 2025Pooled it
- A Mobile App-Based Individualized Nonpharmacological Intervention for Behavioral and Psychological Symptoms in Dementia: Pilot Randomized Controlled Trial.JMIR mHealth and uHealth · 2026Trial
- Machine learning-enabled behavioural and psychological symptoms of dementia management intervention for dementia caregivers: protocol for a hybrid factorial SMART-MRT trial.BMC geriatrics · 2026Article
- The risk of delirium or dementia-related hospitalization among individuals living with dementia after long-term care entry: A population-based risk prediction model.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Altered triple network model connectivity is associated with cognitive function and depressive symptoms in older adults.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Observational
- Assessing Hwa-byung Vulnerability Using the Hwa-byung Personality Scale: a comparative study of machine learning approaches.Journal of pharmacopuncture · 2024Article
- Machine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease models.Cell reports · 2024Article
- Assessment of sleep patterns in dementia and general population cohorts using passive in-home monitoring technologies.Communications medicine · 2024Article
- Effectiveness of a mobile app-based individualized non-pharmacological intervention on behavioral and psychological symptoms of dementia in community-dwelling older adults: Study protocol for a randomized control trial.Journal of Korean gerontological nursing · 2024Article
- Using AI-Based Technologies to Help Nurses Detect Behavioral Disorders: Narrative Literature Review.JMIR nursing · 2024Review
- Article
- Can Sensor Technologies Accurately Detect and Monitor Behavioural and Psychological Symptoms of Dementia? A Systematic Review.Dementia and geriatric cognitive disorders extraReview
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The behavioral and psychological symptoms of dementia (BPSD) are challenging aspects of dementia care. This study used machine learning models to predict the occurrence of BPSD among community-dwelling older adults with dementia. We included 187 older adults with dementia for model training and 35 older adults with dementia for external validation. Demographic and health data and premorbid personality traits were examined at the baseline, and actigraphy was utilized to monitor sleep and activity levels. A symptom diary tracked caregiver-perceived symptom triggers and the daily occurrence of 12 BPSD classified into seven subsyndromes. Several prediction models were also employed, including logistic regression, random forest, gradient boosting machine, and support vector machine. The random forest models revealed the highest area under the receiver operating characteristic curve (AUC) values for hyperactivity, euphoria/elation, and appetite and eating disorders; the gradient boosting machine models for psychotic and affective symptoms; and the support vector machine model showed the highest AUC. The gradient boosting machine model achieved the best performance in terms of average AUC scores across the seven subsyndromes. Caregiver-perceived triggers demonstrated higher feature importance values across the seven subsyndromes than other features. Our findings demonstrate the possibility of predicting BPSD using a machine learning approach.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.