ArticleAlzheimer's research & therapy2024
Prediction of conversion from mild cognitive impairment to Alzheimer's disease and simultaneous feature selection and grouping using Medicaid claim data.
Article in Alzheimer's research & therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Leveraging electronic health records to examine differential clinical outcomes in people with Alzheimer's disease.Communications medicine · 2026Article
- Bidirectional prognostic predictions of conversion and reversion in amnestic mild cognitive impairment.GeroScience · 2026Article
- Predicting progression of Alzheimer's disease using blood-based multi-omics data.Bioinformatics advances · 2026Article
- Yoga for Neurodegenerative Disorders: Therapeutic Effects, Mechanisms, and Applications in Alzheimer's and Parkinson's Disease.Sports (Basel, Switzerland) · 2025Review
- A Pilot Study: The Effect of CPAP Intervention on Sleep Architecture and Cognition in Alzheimer's Disease Patients with Obstructive Sleep Apnea.Neurology international · 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
- Yoga an integrated mind body intervention for improvement in quality of life in individuals with Alzheimer's disease and their caregivers.Frontiers in aging · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDue to the heterogeneity among patients with Mild Cognitive Impairment (MCI), it is critical to predict their risk of converting to Alzheimer's disease (AD) early using routinely collected real-world data such as the electronic health record data or administrative claim data.
methodsThe study used MarketScan Multi-State Medicaid data to construct a cohort of MCI patients. Logistic regression with tree-guided lasso regularization (TGL) was proposed to select important features and predict the risk of converting to AD. A subsampling-based technique was used to extract robust groups of predictive features. Predictive models including logistic regression, generalized random forest, and artificial neural network were trained using the extracted features.
resultsThe proposed TGL workflow selected feature groups that were robust, highly interpretable, and consistent with existing literature. The predictive models using TGL selected features demonstrated higher prediction accuracy than the models using all features or features selected using other methods.
conclusionsThe identified feature groups provide insights into the progression from MCI to AD and can potentially improve risk prediction in clinical practice and trial recruitment.
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Registered trials
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