ArticleFrontiers in aging neuroscience2017
Longitudinal Analysis for Disease Progression via Simultaneous Multi-Relational Temporal-Fused Learning.
Article in Frontiers in aging neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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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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
6 citing papers in PubMed.
- AI for the prediction of early stages of Alzheimer's disease from neuroimaging biomarkers - A narrative review of a growing field.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024Review
- A Tensorized Multitask Deep Learning Network for Progression Prediction of Alzheimer's Disease.Frontiers in aging neuroscience · 2022Article
- Multi-task exclusive relationship learning for alzheimer's disease progression prediction with longitudinal data.Medical image analysis · 2019Article
- Joint regression and classification via relational regularization for Parkinson's disease diagnosis.Technology and health care : official journal of the European Society for Engineering and Medicine · 2018Article
- Sparse feature learning for multi-class Parkinson's disease classification.Technology and health care : official journal of the European Society for Engineering and Medicine · 2018Article
- Multi-task fused sparse learning for mild cognitive impairment identification.Technology and health care : official journal of the European Society for Engineering and Medicine · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
It is highly desirable to predict the progression of Alzheimer's disease (AD) of patients [e.g., to predict conversion of mild cognitive impairment (MCI) to AD], especially longitudinal prediction of AD is important for its early diagnosis. Currently, most existing methods predict different clinical scores using different models, or separately predict multiple scores at different future time points. Such approaches prevent coordinated learning of multiple predictions that can be used to jointly predict clinical scores at multiple future time points. In this paper, we propose a joint learning method for predicting clinical scores of patients using multiple longitudinal prediction models for various future time points. Three important relationships among training samples, features, and clinical scores are explored. The relationship among different longitudinal prediction models is captured using a common feature set among the multiple prediction models at different time points. Our experimental results based on the Alzheimer's disease neuroimaging initiative (ADNI) database shows that our method achieves considerable improvement over competing methods in predicting multiple clinical 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.