ArticleNeurobiology of aging2016
Longitudinal clinical score prediction in Alzheimer's disease with soft-split sparse regression based random forest.
Article in Neurobiology of aging, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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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.
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Who cites it
40 citing papers in PubMed, 114 citations in OpenAlex.
- Effect sizes for brain-based predictive models of cognition across the lifespan.Communications psychology · 2026Review
- Connectome-based prediction of future episodic memory performance for individual amnestic mild cognitive impairment patients.Brain communications · 2025Article
- Deep multimodal learning for domain-level cognitive decline prediction in Alzheimer's disease.Frontiers in artificial intelligence · 2025Article
- Baseline multimodal imaging to predict longitudinal clinical decline in atypical Alzheimer's disease.Cortex; a journal devoted to the study of the nervous system and behavior · 2024Article
- Intelligent prediction of Alzheimer's disease via improved multifeature squeeze-and-excitation-dilated residual network.Scientific reports · 2024Article
- TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance.Medical image analysis · 2024Article
- Machine learning in neurological disorders: A multivariate LSTM and AdaBoost approach to Alzheimer's disease time series analysis.Health care science · 2024Article
- Algorithmic Fairness of Machine Learning Models for Alzheimer Disease Progression.JAMA network open · 2023Article
- Computer aided progression detection model based on optimized deep LSTM ensemble model and the fusion of multivariate time series data.Scientific reports · 2023Article
- A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer's disease.Brain informatics · 2023Article
- PPAD: a deep learning architecture to predict progression of Alzheimer's disease.Bioinformatics (Oxford, England) · 2023Article
- Predictors of rapid eye movement sleep behavior disorder in patients with Parkinson's disease based on random forest and decision tree.PloS one · 2022Article
- A Tensorized Multitask Deep Learning Network for Progression Prediction of Alzheimer's Disease.Frontiers in aging neuroscience · 2022Article
- Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data.Emerging topics in life sciences · 2021Review
- Multi-Resemblance Multi-Target Low-Rank Coding for Prediction of Cognitive Decline With Longitudinal Brain Images.IEEE transactions on medical imaging · 2021Article
- Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review.JAMIA open · 2021Review
- A random forest method with feature selection for developing medical prediction models with clustered and longitudinal data.Journal of biomedical informatics · 2021Article
- AD Course Map charts Alzheimer's disease progression.Scientific reports · 2021Article
- Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults.The journals of gerontology. Series A, Biological sciences and medical sciences · 2021Article
- Efficacy of the combined use of donepezil with either quetiapine or sodium valproate in patients with Alzheimer's disease with behavioral and psychological symptoms of dementia, and their effects on vascular endothelial growth factors.Experimental and therapeutic medicine · 2021Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 2 countries.
Funding
Abstract
Alzheimer's disease (AD) is an irreversible neurodegenerative disease and affects a large population in the world. Cognitive scores at multiple time points can be reliably used to evaluate the progression of the disease clinically. In recent studies, machine learning techniques have shown promising results on the prediction of AD clinical scores. However, there are multiple limitations in the current models such as linearity assumption and missing data exclusion. Here, we present a nonlinear supervised sparse regression-based random forest (RF) framework to predict a variety of longitudinal AD clinical scores. Furthermore, we propose a soft-split technique to assign probabilistic paths to a test sample in RF for more accurate predictions. In order to benefit from the longitudinal scores in the study, unlike the previous studies that often removed the subjects with missing scores, we first estimate those missing scores with our proposed soft-split sparse regression-based RF and then utilize those estimated longitudinal scores at all the previous time points to predict the scores at the next time point. The experiment results demonstrate that our proposed method is superior to the traditional RF and outperforms other state-of-art regression models. Our method can also be extended to be a general regression framework to predict other disease scores.
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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.