ArticlePloS one2020
Deep neural network models for identifying incident dementia using claims and EHR datasets.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Electronic health record-based prediction models for dementia detection: a systematic review of model performance and quality.Journal of the American Medical Informatics Association : JAMIA · 2026Pooled it
- Use of Machine Learning and Artificial Intelligence Methods in Geriatric Mental Health Research Involving Electronic Health Record or Administrative Claims Data: A Systematic Review.Frontiers in psychiatry · 2021Pooled it
- Developing a novel algorithm to identify incident and prevalent dementia in Medicare claims-the ARIC Study.American journal of epidemiology · 2025Article
- Accuracy of Large Language Models to Identify Stroke Subtypes Within Unstructured Electronic Health Record Data.Stroke · 2025Article
- Real-world health care costs and resource utilization associated with mild cognitive impairment in the United States: A retrospective cohort study of commercial and Medicare data.Journal of managed care & specialty pharmacy · 2025Article
- Cognitive performance classification of older patients using machine learning and electronic medical records.Scientific reports · 2025Article
- Predicting the onset of Alzheimer's disease and related dementia using electronic health records: findings from the cache county study on memory in aging (1995-2008).BMC medical informatics and decision making · 2024Article
- Predicting the onset of Alzheimer's disease and related dementia using Electronic Health Records: Findings from the Cache County Study on Memory in Aging (1995-2008).Research square · 2024Article
- Semi-supervised ROC analysis for reliable and streamlined evaluation of phenotyping algorithms.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- A neural network approach to predict opioid misuse among previously hospitalized patients using electronic health records.PloS one · 2024Article
- Identifying Medicare beneficiaries with Alzheimer's disease and related dementia using home health OASIS assessments.Journal of the American Geriatrics Society · 2023Article
- Dementia and electronic health record phenotypes: a scoping review of available phenotypes and opportunities for future research.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Machine learning approaches for electronic health records phenotyping: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Prediction of Incident Dementia Using Patient Temporal Health Status.Studies in health technology and informatics · 2022Article
- A weighted patient network-based framework for predicting chronic diseases using graph neural networks.Scientific reports · 2021Article
- A 5-min Cognitive Task With Deep Learning Accurately Detects Early Alzheimer's Disease.Frontiers in aging neuroscience · 2020Article
- Ethical issues when using digital biomarkers and artificial intelligence for the early detection of dementia.Wiley interdisciplinary reviews. Data mining and knowledge discoveryReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study investigates the use of deep learning methods to improve the accuracy of a predictive model for dementia, and compares the performance to a traditional machine learning model. With sufficient accuracy the model can be deployed as a first round screening tool for clinical follow-up including neurological examination, neuropsychological testing, imaging and recruitment to clinical trials. Seven cohorts with two years of data, three to eight years prior to index date, and an incident cohort were created. Four trained models for each cohort, boosted trees, feed forward network, recurrent neural network and recurrent neural network with pre-trained weights, were constructed and their performance compared using validation and test data. The incident model had an AUC of 94.4% and F1 score of 54.1%. Eight years removed from index date the AUC and F1 scores were 80.7% and 25.6%, respectively. The results for the remaining cohorts were between these ranges. Deep learning models can result in significant improvement in performance but come at a cost in terms of run times and hardware requirements. The results of the model at index date indicate that this modeling can be effective at stratifying patients at risk of dementia. At this time, the inability to sustain this quality at longer lead times is more an issue of data availability and quality rather than one of algorithm choices.
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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.