ArticleJournal of biomedical informatics2023
AD-BERT: Using pre-trained language model to predict the progression from mild cognitive impairment to Alzheimer's disease.
Article in Journal of biomedical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Artificial Intelligence for Alzheimer's Disease Diagnosis: From Traditional Machine Learning to Large Language Models.Biosensors · 2026Review
- Centralized Review of Alzheimer's Disease and Related Dementias Biomedical Repositories and Computational Methods.Bioengineering (Basel, Switzerland) · 2026Review
- Combining generative large language models and pretrained language models for predicting acute respiratory distress syndrome progression.Medical & biological engineering & computing · 2026Article
- Advancing Alzheimer Disease Prediction With Large Language Model-Based Linguistic Feature Analysis: Development and Validation Study.JMIR medical informatics · 2026Article
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Review of Artificial Intelligence for Clinical Use in Alzheimer's Disease and Related Dementias.Seminars in neurology · 2026Review
- Systematic review of unveiling the potential of AI using machine learning and deep learning methods in neurodegenerative diseases.PeerJ. Computer science · 2026Review
- Vision and convolutional transformers for Alzheimer's disease diagnosis: a systematic review of architectures, multimodal fusion and critical gaps.Brain informatics · 2025Review
- Development and validation of a pre-trained language model for neonatal morbidities: a retrospective, multicentre, prognostic study.The Lancet. Digital health · 2025Article
- Characterizing Dementia Phenotypes from Unstructured EHR Notes with Generative AI and Interpretable Machine Learning.medRxiv : the preprint server for health sciences · 2025Article
- NeuroNet-AD: A Multimodal Deep Learning Framework for Multiclass Alzheimer's Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2025Article
- AI-powered model for accurate prediction of MCI-to-AD progression.Acta pharmaceutica Sinica. B · 2025Article
- Artificial intelligence-driven natural language processing for identifying linguistic patterns in Alzheimer's disease and mild cognitive impairment: A study of lexical, syntactic, and cohesive features of speech through picture description tasks.Journal of Alzheimer's disease : JAD · 2025Article
- CD-Tron: Leveraging large clinical language model for early detection of cognitive decline from electronic health records.Journal of biomedical informatics · 2025Article
- Deep Learning Model for Natural Language to Assess Effectiveness of Patients With Non-Muscle Invasive Bladder Cancer Receiving Intravesical Bacillus Calmette-Guérin Therapy.JCO clinical cancer informatics · 2025Article
- Resistance training protects the hippocampus and precuneus against atrophy and benefits white matter integrity in older adults with mild cognitive impairment.GeroScience · 2025Article
- CD-Tron: Leveraging Large Clinical Language Model for Early Detection of Cognitive Decline from Electronic Health Records.medRxiv : the preprint server for health sciences · 2025Article
- Automated identification of fall-related injuries in unstructured clinical notes.American journal of epidemiology · 2025Article
- Federated learning with multi-cohort real-world data for predicting the progression from mild cognitive impairment to Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- The foundational capabilities of large language models in predicting postoperative risks using clinical notes.NPJ digital medicine · 2025Article
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Authors and funding
13 authors.
Funding
Abstract
objectiveWe develop a deep learning framework based on the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model using unstructured clinical notes from electronic health records (EHRs) to predict the risk of disease progression from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD).
methodsWe identified 3657 patients diagnosed with MCI together with their progress notes from Northwestern Medicine Enterprise Data Warehouse (NMEDW) between 2000 and 2020. The progress notes no later than the first MCI diagnosis were used for the prediction. We first preprocessed the notes by deidentification, cleaning and splitting into sections, and then pre-trained a BERT model for AD (named AD-BERT) based on the publicly available Bio+Clinical BERT on the preprocessed notes. All sections of a patient were embedded into a vector representation by AD-BERT and then combined by global MaxPooling and a fully connected network to compute the probability of MCI-to-AD progression. For validation, we conducted a similar set of experiments on 2563 MCI patients identified at Weill Cornell Medicine (WCM) during the same timeframe.
resultsCompared with the 7 baseline models, the AD-BERT model achieved the best performance on both datasets, with Area Under receiver operating characteristic Curve (AUC) of 0.849 and F1 score of 0.440 on NMEDW dataset, and AUC of 0.883 and F1 score of 0.680 on WCM dataset.
conclusionThe use of EHRs for AD-related research is promising, and AD-BERT shows superior predictive performance in modeling MCI-to-AD progression prediction. Our study demonstrates the utility of pre-trained language models and clinical notes in predicting MCI-to-AD progression, which could have important implications for improving early detection and intervention for AD.
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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.