ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2021
Extraction of Active Medications and Adherence Using Natural Language Processing for Glaucoma Patients.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Natural language processing to enhance rheumatoid arthritis care in clinical studies: a scoping review of applications, data, approaches, challenges and future directions.Rheumatology international · 2026Article
- Identification of Risk Factors for Glaucoma Progression in Free-Text Clinical Notes Using a Local Large Language Model.Translational vision science & technology · 2026Article
- Ontology-Based Medication Named Entity Recognition Using Pretrained Transformer Models From a Thai Hospital: Model Fine-Tuning and Validation Study.JMIR formative research · 2026Article
- Ophthalmic care at an academic medical centre for patients who were incarcerated or in immigration detention.Eye (London, England) · 2025Observational
- Clinical applications of large language models in medicine and surgery: A scoping review.The Journal of international medical research · 2025Article
- Prediction of multiclass surgical outcomes in glaucoma using multimodal deep learning based on free-text operative notes and structured EHR data.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Assessing user perspectives on clinical pharmacogenomics consultation documentation: a user-centered evaluation.Frontiers in pharmacology · 2024Article
- Named Entity Recognition in Electronic Health Records: A Methodological Review.Healthcare informatics research · 2023Article
- Development of an Open-Source Annotated Glaucoma Medication Dataset From Clinical Notes in the Electronic Health Record.Translational vision science & technology · 2022Article
- Applications of natural language processing in ophthalmology: present and future.Frontiers in medicine · 2022Review
- Big data to guide glaucoma treatment.Taiwan journal of ophthalmologyReview
- Development and Evaluation of a Computable Phenotype for Normal Tension Glaucoma.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Accuracy of medication data in electronic health records (EHRs) is crucial for patient care and research, but many studies have shown that medication lists frequently contain errors. In contrast, physicians often pay more attention to the clinical notes and record medication information in them. The medication information in notes may be used for medication reconciliation to improve the medication lists' accuracy. However, accurately extracting patient's current medications from free-text narratives is challenging. In this study, we first explored the discrepancies between medication documentation in medication lists and progress notes for glaucoma patients by manually reviewing patients' charts. Next, we developed and validated a named entity recognition model to identify current medication and adherence from progress notes. Lastly, a prototype tool for medication reconciliation using the developed model was demonstrated. In the future, the model has the potential to be incorporated into the EHR system to help with realtime medication reconciliation.
Indexed as
Identifiers
35308943PMC8861739What 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.