ArticleInternational journal of medical informatics2019
Automatic extraction and assessment of lifestyle exposures for Alzheimer's disease using natural language processing.
Article in International journal of medical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Natural language processing for geriatric syndromes: a systematic review of methods, applications, and challenges.BMC medical informatics and decision making · 2026Pooled it
- Fine-Tuning Large Language Models for Effective Nutrition Support in Residential Aged Care: A Domain Expertise Approach.Healthcare (Basel, Switzerland) · 2025Article
- Clinical applications of large language models in medicine and surgery: A scoping review.The Journal of international medical research · 2025Article
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- Empowering Precision Medicine for Rare Diseases through Cloud Infrastructure Refactoring.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Digital Neuropsychology beyond Computerized Cognitive Assessment: Applications of Novel Digital Technologies.Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists · 2024Review
- Developing an ontology of non-pharmacological treatment for emotional and mood disturbances in dementia.Scientific reports · 2024Article
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- Classifying the lifestyle status for Alzheimer's disease from clinical notes using deep learning with weak supervision.BMC medical informatics and decision making · 2022Article
- Evaluation of Doc'EDS: a French semantic search tool to query health documents from a clinical data warehouse.BMC medical informatics and decision making · 2022Article
- Artificial intelligence in clinical and translational science: Successes, challenges and opportunities.Clinical and translational science · 2022Review
- Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review.JAMIA open · 2021Review
- Social and Behavioral Determinants of Health in the Era of Artificial Intelligence with Electronic Health Records: A Scoping Review.Health data science · 2021Article
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Authors and funding
6 authors.
Funding
Abstract
introductionPrevious biomedical studies identified many lifestyle exposures that could possibly represent risk factors for dementia in general or dementia due to Alzheimer's disease (AD). These lifestyle exposures are mainly mentioned in free-text electronic health records (EHRs). However, automatic extraction and assessment of these exposures using EHRs remains understudied.
methodsA natural language processing (NLP) approach was adopted to extract lifestyle exposures and intervention strategies from the clinical notes of 260 patients with clinical diagnoses of AD dementia and 260 age-matched cognitively unimpaired persons. Statistics of lifestyle exposures were compared between these two groups. The mapping results of the NLP extraction were evaluated by comparing the results with data captured independently by clinicians.
resultsThirty out of fifty-five potentially relevant lifestyle exposures were mentioned in our clinical note dataset. Twenty-two dietary factors and three substance abuses that were potentially relevant were not found in clinical notes. Patients with AD dementia were significantly exposed to more of the potential risk factors compared to the cognitively unimpaired subjects (χ2 = 120.31, p-value < 0.001). The average accuracy of the automated extraction was 74.0% in comparison with the manual review of randomly selected 50 sample documents. DISCUSSION AND
conclusionWe illustrated the feasibility of NLP techniques for the automated evaluation of a large number lifestyle habits using free-text EHR data. We found that AD dementia patients were exposed to more of the potential risk factors than the comparison group. Our results also demonstrated the feasibility and accuracy of investigating putative risk factors using NLP techniques.
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