ArticleNPJ digital medicine2022
A high-generalizability machine learning framework for predicting the progression of Alzheimer's disease using limited data.
Article in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Clinical prediction models using artificial intelligence approaches in dementia.Aging clinical and experimental research · 2025Pooled it
- A meta-learning method with reduced data requirements for training and updating deep learning models on wearable devices.Communications medicine · 2026Article
- Robust Longitudinal Dementia Prediction under Systemic Missingness via Hierarchical Fusion and Test-Time Adaptation.medRxiv : the preprint server for health sciences · 2026Article
- GLP-1 agonists in neurodegeneration: a multimodal biomarker-guided approach.Trends in molecular medicine · 2026Review
- Wearable sensor technology and machine learning for anterior cruciate ligament injury diagnosis: an exploratory feasibility study.BMJ open · 2026Article
- HyperTransFusion: a hypernetwork transformer with black winged kite optimization for multimodal early Alzheimer's disease diagnosis.Frontiers in digital health · 2026Article
- Artificial neural networks fighting real neural decline: a systematic review of AI in Alzheimer's research.Artificial intelligence review · 2026Article
- Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine.Psychoradiology · 2026Review
- A simple nomogram to predict the onset of dementia in mild cognitive impairment: A retrospective study.Journal of Alzheimer's disease : JAD · 2025Article
- High-resolution conditional MR image synthesis through the PACGAN framework.Scientific reports · 2025Article
- Cross-Dataset Evaluation of Dementia Longitudinal Progression Prediction Models.Human brain mapping · 2025Article
- Deep Learning-based Hierarchical Brain Segmentation with Preliminary Analysis of the Repeatability and Reproducibility.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- An end-to-end interpretable machine-learning-based framework for early-stage diagnosis of gallbladder cancer using multi-modality medical data.BMC cancer · 2025Article
- Cross-dataset Evaluation of Dementia Longitudinal Progression Prediction Models.medRxiv : the preprint server for health sciences · 2025Article
- Strategies to Improve the Robustness and Generalizability of Deep Learning Segmentation and Classification in Neuroimaging.BioMedInformatics · 2025Article
- Adaptable graph neural networks design to support generalizability for clinical event prediction.Journal of biomedical informatics · 2025Article
- Cognitive performance classification of older patients using machine learning and electronic medical records.Scientific reports · 2025Article
- Leveraging machine learning to enhance appointment adherence at a novel post-discharge care transition clinic.JAMIA open · 2024Article
- Understanding of Wetting Mechanism Toward the Sticky Powder and Machine Learning in Predicting Granule Size Distribution Under High Shear Wet Granulation.AAPS PharmSciTech · 2024Article
- HiMAL: Multimodal Hierarchical Multi-task Auxiliary Learning framework for predicting Alzheimer's disease progression.JAMIA open · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Alzheimer's disease is a neurodegenerative disease that imposes a substantial financial burden on society. A number of machine learning studies have been conducted to predict the speed of its progression, which varies widely among different individuals, for recruiting fast progressors in future clinical trials. However, because the data in this field are very limited, two problems have yet to be solved: the first is that models built on limited data tend to induce overfitting and have low generalizability, and the second is that no cross-cohort evaluations have been done. Here, to suppress the overfitting caused by limited data, we propose a hybrid machine learning framework consisting of multiple convolutional neural networks that automatically extract image features from the point of view of brain segments, which are relevant to cognitive decline according to clinical findings, and a linear support vector classifier that uses extracted image features together with non-image information to make robust final predictions. The experimental results indicate that our model achieves superior performance (accuracy: 0.88, area under the curve [AUC]: 0.95) compared with other state-of-the-art methods. Moreover, our framework demonstrates high generalizability as a result of evaluations using a completely different cohort dataset (accuracy: 0.84, AUC: 0.91) collected from a different population than that used for training.
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.