ArticleJournal of Alzheimer's disease : JAD2020
Validation of Random Forest Machine Learning Models to Predict Dementia-Related Neuropsychiatric Symptoms in Real-World Data.
Article in Journal of Alzheimer's disease : JAD, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 35 citations in OpenAlex.
- Pooled it
- Use of Machine Learning and Artificial Intelligence Methods in Geriatric Mental Health Research Involving Electronic Health Record or Administrative Claims Data: A Systematic Review.Frontiers in psychiatry · 2021Pooled it
- Novel bayesian nonparametric unsupervised learning approach to precision symptom management in cancer survivors: a re-analysis of a comparative effectiveness trial.Journal of behavioral medicine · 2026Article
- Sex differences in documented clinical features of memory clinic patients: a natural language processing study.Cerebral circulation - cognition and behavior · 2026Article
- 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
- Case Identification of Depression in Inpatient Electronic Medical Records: Scoping Review.JMIR medical informatics · 2024Article
- Using AI-Based Technologies to Help Nurses Detect Behavioral Disorders: Narrative Literature Review.JMIR nursing · 2024Review
- Comorbidity and household income as mediators of gender inequalities in dementia risk: a real-world data population study.BMC geriatrics · 2024Observational
- Disparities by Socioeconomic Status and Diagnosis of Dementia in the Prescribing of Antipsychotics in a Real-World Data Population Over 60 Years of Age.Journal of Alzheimer's disease : JAD · 2024Observational
- The reporting of neuropsychiatric symptoms in electronic health records of individuals with Alzheimer's disease: a natural language processing study.Alzheimer's research & therapy · 2023Article
- Neuropsychiatric Symptoms and Commonly Used Biomarkers of Alzheimer's Disease: A Literature Review from a Machine Learning Perspective.Journal of Alzheimer's disease : JAD · 2023Review
- Long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning methods.Scientific reports · 2022Article
- Changes in Social and Clinical Determinants of COVID-19 Outcomes Achieved by the Vaccination Program: A Nationwide Cohort Study.International journal of environmental research and public health · 2022Article
- Dementia Risk Score for a Population in Southern Europe Calculated Using Competing Risk Models.Journal of Alzheimer's disease : JAD · 2022Observational
- Use of Patient-Reported Symptoms from an Online Symptom Tracking Tool for Dementia Severity Staging: Development and Validation of a Machine Learning Approach.Journal of medical Internet research · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNeuropsychiatric symptoms (NPS) are the leading cause of the social burden of dementia but their role is underestimated.
objectiveThe objective of the study was to validate predictive models to separately identify psychotic and depressive symptoms in patients diagnosed with dementia using clinical databases representing the whole population to inform decision-makers.
methodsFirst, we searched the electronic health records of 4,003 patients with dementia to identify NPS. Second, machine learning (random forest) algorithms were applied to build separate predictive models for psychotic and depressive symptom clusters in the training set (N = 3,003). Third, calibration and discrimination were assessed in the test set (N = 1,000) to assess the performance of the models.
resultsNeuropsychiatric symptoms were noted in the electronic health record of 58% of patients. The area under the receiver operating curve reached 0.80 for the psychotic cluster model and 0.74 for the depressive cluster model. The Kappa index and accuracy also showed better discrimination in the psychotic model. Calibration plots indicated that both types of model had less predictive accuracy when the probability of neuropsychiatric symptoms was <25%. The most important variables in the psychotic cluster model were use of risperidone, level of sedation, use of quetiapine and haloperidol and the number of antipsychotics prescribed. In the depressive cluster model, the most important variables were number of antidepressants prescribed, escitalopram use, level of sedation, and age.
conclusionGiven their relatively good performance, the predictive models can be used to estimate prevalence of NPS in population databases.
Indexed as
Identifiers
What OpenQuestion holds
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.