ArticlePloS one2019
Identifying incident dementia by applying machine learning to a very large administrative claims dataset.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 3 of them syntheses that pooled it.
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Who cites it
40 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Discriminative performance of externally validated dementia risk prediction models: a systematic review and meta-analysis.BMC medicine · 2026Pooled it
- Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions.Journal of medical systems · 2023Pooled 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
- A Flexible-Horizon Clinical Decision Support Model for Dementia Early Detection and Risk Prediction in Secondary Care: A Responsible AI Approach.Journal of medical systems · 2026Article
- Developing a novel algorithm to identify incident and prevalent dementia in Medicare claims-the ARIC Study.American journal of epidemiology · 2025Article
- Novel predictors of Alzheimer's disease in Down syndrome identified using machine learning.Journal of Alzheimer's disease : JAD · 2025Article
- Bridging Gaps in Sundown Syndrome Research: a Scoping Review and Roadmap for Future Multimodal Approaches.Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists · 2025Article
- Predicting Alzheimer's Disease Diagnosis, a Decade or more Years before Onset using the Electronic Health Record and Random Forest Machine Learning Models.medRxiv : the preprint server for health sciences · 2025Article
- XGBoost models based on non imaging features for the prediction of mild cognitive impairment in older adults.Scientific reports · 2025Article
- Real-world health care costs and resource utilization associated with mild cognitive impairment in the United States: A retrospective cohort study of commercial and Medicare data.Journal of managed care & specialty pharmacy · 2025Article
- Differentiation Between Early and Severe Stages of Dementia in Claims Data Based on Diagnosis, Prescription, and Utilization Patterns.Neurology and therapy · 2025Article
- Development and validation of a patient-level model to predict dementia across a network of observational databases.BMC medicine · 2024Article
- Psychiatric Disease as a Potential Risk Factor for Dementia: A Narrative Review.Brain sciences · 2024Review
- Artificial intelligence approaches for early detection of neurocognitive disorders among older adults.Frontiers in computational neuroscience · 2024Article
- Life-Course Multidisciplinary Psychosocial Predictors of Dementia Among Older Adults: Results From the Health and Retirement Study.Innovation in aging · 2024Article
- Article
- Uncovering the burden of hidradenitis suppurativa misdiagnosis and underdiagnosis: a machine learning approach.Frontiers in medical technology · 2024Article
- Identifying Medicare beneficiaries with Alzheimer's disease and related dementia using home health OASIS assessments.Journal of the American Geriatrics Society · 2023Article
- HomeADScreen: Developing Alzheimer's disease and related dementia risk identification model in home healthcare.International journal of medical informatics · 2023Article
- Using Hypothesis-Led Machine Learning and Hierarchical Cluster Analysis to Identify Disease Pathways Prior to Dementia: Longitudinal Cohort Study.Journal of medical Internet research · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease and related dementias (ADRD) are highly prevalent conditions, and prior efforts to develop predictive models have relied on demographic and clinical risk factors using traditional logistical regression methods. We hypothesized that machine-learning algorithms using administrative claims data may represent a novel approach to predicting ADRD. Using a national de-identified dataset of more than 125 million patients including over 10,000 clinical, pharmaceutical, and demographic variables, we developed a cohort to train a machine learning model to predict ADRD 4-5 years in advance. The Lasso algorithm selected a 50-variable model with an area under the curve (AUC) of 0.693. Top diagnosis codes in the model were memory loss (780.93), Parkinson's disease (332.0), mild cognitive impairment (331.83) and bipolar disorder (296.80), and top pharmacy codes were psychoactive drugs. Machine learning algorithms can rapidly develop predictive models for ADRD with massive datasets, without requiring hypothesis-driven feature engineering.
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