ReviewJAMIA open2024
Natural language processing systems for extracting information from electronic health records about activities of daily living. A systematic review.
Review in JAMIA open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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.
- Quality of Life of People Living With Dementia Residing in Nursing Homes: Secondary Analysis of Observational Data.Journal of medical Internet research · 2026Article
- AI-Assisted Clinical Data Abstraction From Electronic Health Records: Retrospective Concordance Study.JMIR formative research · 2026Article
- Artificial Intelligence in Infectious Disease Care: Selected Applications in Tuberculosis, Sepsis, and Antimicrobial Stewardship.Diagnostics (Basel, Switzerland) · 2026Review
- Developing an AI platform for voice-input medical records in Polish: a digital application for optimisation of physicians' workload.Scientific reports · 2026Article
- Leveraging AI solutions for sustainable practice in pediatric radiology: a practical guide and an educational tool.Pediatric radiology · 2026Review
- Artificial Intelligence in Venous Thromboembolism Prevention: A Narrative Review of Machine Learning, Deep Learning, and Natural Language Processing.Journal of cardiovascular development and disease · 2026Review
- The role of artificial intelligence in managing COVID-19 and long COVID: a narrative review.Osong public health and research perspectives · 2026Article
- WHF Roadmap for Integrated Care in People Living with - or at Risk of - Cardiovascular Disease and Multiple Long-Term Conditions.Global heart · 2026Review
- Applying NLP methods to code functional performance in electronic health records using the international classification of functioning, disability, and health.Disability and health journal · 2025Article
- From manual clinical criteria to machine learning algorithms: Comparing outcome endpoints derived from diverse electronic health record data modalities.PLOS digital health · 2025Article
- Qualitative changes in clinical records after implementation of pharmacist-led antimicrobial stewardship program: a text mining analysis.Journal of pharmaceutical health care and sciences · 2025Article
- Global Research Trends, Hotspots, Impacts, and Emergence of Artificial Intelligence and Machine Learning in Health and Medicine: A 25-Year Bibliometric Analysis.Healthcare (Basel, Switzerland) · 2025Review
- A Tutorial and Use Case Example of the eXtreme Gradient Boosting (XGBoost) Artificial Intelligence Algorithm for Drug Development Applications.Clinical and translational science · 2025Article
- Identifying health conditions in older adults in textual health records using deep learning-based natural language processing.Computational and structural biotechnology journal · 2025Article
- Bibliometric analysis of artificial intelligence in healthcare research: Trends and future directions.Future healthcare journal · 2024Article
- ChatGPT-4 extraction of heart failure symptoms and signs from electronic health records.Progress in cardiovascular diseasesArticle
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Natural language processing (NLP) can enhance research on activities of daily living (ADL) by extracting structured information from unstructured electronic health records (EHRs) notes. This review aims to give insight into the state-of-the-art, usability, and performance of NLP systems to extract information on ADL from EHRs. Materials and Methods: A systematic review was conducted based on searches in Pubmed, Embase, Cinahl, Web of Science, and Scopus. Studies published between 2017 and 2022 were selected based on predefined eligibility criteria. Results: The review identified 22 studies. Most studies (65%) used NLP for classifying unstructured EHR data on 1 or 2 ADL. Deep learning, combined with a ruled-based method or machine learning, was the approach most commonly used. NLP systems varied widely in terms of the pre-processing and algorithms. Common performance evaluation methods were cross-validation and train/test datasets, with F1, precision, and sensitivity as the most frequently reported evaluation metrics. Most studies reported relativity high overall scores on the evaluation metrics. Discussion: NLP systems are valuable for the extraction of unstructured EHR data on ADL. However, comparing the performance of NLP systems is difficult due to the diversity of the studies and challenges related to the dataset, including restricted access to EHR data, inadequate documentation, lack of granularity, and small datasets. Conclusion: This systematic review indicates that NLP is promising for deriving information on ADL from unstructured EHR notes. However, what the best-performing NLP system is, depends on characteristics of the dataset, research question, and type of ADL.
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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.