ArticleFrontiers in big data2021
Developing an Explainable Machine Learning-Based Personalised Dementia Risk Prediction Model: A Transfer Learning Approach With Ensemble Learning Algorithms.
Article in Frontiers in big data, 2021. 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, 65 citations in OpenAlex.
- Interpretable machine learning for dementia: A systematic review.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Pooled 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
- Use of explainable AI (xAI) in dementia detection and prognosis: a scoping review.BMC medical informatics and decision making · 2026Article
- Predicting 5-Year Survival and Mortality in Dementia Patients: A Data-Driven Approach Using XGBoost for Enhanced Care and Resource Allocation.Psychiatry investigation · 2025Article
- Advancing Alzheimer's Therapy: Computational strategies and treatment innovations.IBRO neuroscience reports · 2025Review
- Dementia classification using two-channel electroencephalography features.Scientific reports · 2025Article
- CAREUP: An Integrated Care Platform with Intrinsic Capacity Monitoring and Prediction Capabilities.Sensors (Basel, Switzerland) · 2025Article
- Persistent homology for MCI classification: a comparative analysis between graph and Vietoris-Rips filtrations.Frontiers in neuroscience · 2025Article
- Bias in machine learning applications to address non-communicable diseases at a population-level: a scoping review.BMC public health · 2024Article
- Integrating Demographics and Imaging Features for Various Stages of Dementia Classification: Feed Forward Neural Network Multi-Class Approach.Biomedicines · 2024Article
- Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer's disease detection.Brain informatics · 2024Review
- An explainable machine learning approach for Alzheimer's disease classification.Scientific reports · 2024Article
- Life-Course Multidisciplinary Psychosocial Predictors of Dementia Among Older Adults: Results From the Health and Retirement Study.Innovation in aging · 2024Article
- Artificial intelligence for dementia prevention.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Review
- Artificial intelligence for dementia-Applied models and digital health.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Review
- Diagnostic Efficacy and Clinical Relevance of Artificial Intelligence in Detecting Cognitive Decline.Cureus · 2023Review
- Article
- Explaining graph convolutional network predictions for clinicians-An explainable AI approach to Alzheimer's disease classification.Frontiers in artificial intelligence · 2023Article
- An explainable machine learning based prediction model for Alzheimer's disease in China longitudinal aging study.Frontiers in aging neuroscience · 2023Article
- A Machine Learning Approach for Early Diagnosis of Cognitive Impairment Using Population-Based Data.Journal of Alzheimer's disease : JAD · 2023Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Alzheimer's disease (AD) has its onset many decades before dementia develops, and work is ongoing to characterise individuals at risk of decline on the basis of early detection through biomarker and cognitive testing as well as the presence/absence of identified risk factors. Risk prediction models for AD based on various computational approaches, including machine learning, are being developed with promising results. However, these approaches have been criticised as they are unable to generalise due to over-reliance on one data source, poor internal and external validations, and lack of understanding of prediction models, thereby limiting the clinical utility of these prediction models. We propose a framework that employs a transfer-learning paradigm with ensemble learning algorithms to develop explainable personalised risk prediction models for dementia. Our prediction models, known as
Indexed as
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.