ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2019
Deep Learning Prediction of Mild Cognitive Impairment using Electronic Health Records.
Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses 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
20 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer's Disease Not Using Neuroimaging Biomarkers: A Systematic Review.Journal of Alzheimer's disease : JAD · 2024Pooled it
- Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions.Journal of medical systems · 2023Pooled it
- Pooled it
- Predicting Alzheimer's Disease Diagnosis, a Decade or more Years before Onset using the Electronic Health Record and Random Forest Machine Learning Models.medRxiv : the preprint server for health sciences · 2025Article
- AI-powered model for accurate prediction of MCI-to-AD progression.Acta pharmaceutica Sinica. B · 2025Article
- Causal Explanation from Mild Cognitive Impairment Progression using Graph Neural Networks.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2024Article
- Characterizing the progression from mild cognitive impairment to dementia: a network analysis of longitudinal clinical visits.BMC medical informatics and decision making · 2024Article
- The Use of Deep Learning and Machine Learning on Longitudinal Electronic Health Records for the Early Detection and Prevention of Diseases: Scoping Review.Journal of medical Internet research · 2024Article
- Employing Deep-Learning Approach for the Early Detection of Mild Cognitive Impairment Transitions through the Analysis of Digital Biomarkers.Sensors (Basel, Switzerland) · 2023Article
- Machine learning prediction of mild cognitive impairment and its progression to Alzheimer's disease.Health science reports · 2023Article
- Assessment of Prediction Tasks and Time Window Selection in Temporal Modeling of Electronic Health Record Data: a Systematic Review.Journal of healthcare informatics research · 2023Article
- Machine learning functional impairment classification with electronic health record data.Journal of the American Geriatrics Society · 2023Article
- AD-BERT: Using pre-trained language model to predict the progression from mild cognitive impairment to Alzheimer's disease.Journal of biomedical informatics · 2023Article
- Intrinsic Capacity to Predict Future Adverse Health Outcomes in Older Adults: A Scoping Review.Healthcare (Basel, Switzerland) · 2023Article
- Mild Cognitive Impairment: Data-Driven Prediction, Risk Factors, and Workup.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Detection of Dementia Signals from Longitudinal Clinical Visits Using One-Class Classification.Proceedings. IEEE International Conference on Healthcare Informatics · 2022Article
- Risk Models Based on Non-Cognitive Measures May Identify Presymptomatic Alzheimer's Disease.Journal of Alzheimer's disease : JAD · 2022Article
- A dominant set-informed interpretable fuzzy system for automated diagnosis of dementia.Frontiers in neuroscience · 2022Article
- Early Alert of Elderly Cognitive Impairment using Temporal Streaming Clustering.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2021Article
- Artificial intelligence algorithms for the prediction of cognitive decline and dementia: A theoretical-historical narrative review.Journal of Alzheimer's disease reportsReview
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
About 44.4 million people have been diagnosed with dementia worldwide, and it is estimated that this number will be almost tripled by 2050. Predicting mild cognitive impairment (MCI), an intermediate state between normal cognition and dementia and an important risk factor for the development of dementia is crucial in aging populations. MCI is formally determined by health professionals through a comprehensive cognitive evaluation, together with a clinical examination, medical history and often the input of an informant (an individual that know the patient very well). However, this is not routinely performed in primary care visits, and could result in a significant delay in diagnosis. In this study, we used deep learning and machine learning techniques to predict the progression from cognitively unimpaired to MCI and also to analyze the potential for patient clustering using routinely-collected electronic health records (EHRs). Our analysis of EHRs indicates that temporal characteristics of patient data incorporated in a deep learning model provides increased power in predicting MCI.
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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.