ArticleSeminars in arthritis and rheumatism2021
Topic modeling to characterize the natural history of ANCA-Associated vasculitis from clinical notes: A proof of concept study.
Article in Seminars in arthritis and rheumatism, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed, 17 citations in OpenAlex.
- Identification of an ANCA-Associated Vasculitis Cohort Using Deep Learning and Electronic Health Records.medRxiv : the preprint server for health sciences · 2024Article
- Mapping two decades of research in rheumatology-specific journals: a topic modeling analysis with BERTopic.Therapeutic advances in musculoskeletal disease · 2024Article
- Capturing Concerns about Patient Deterioration in Narrative Documentation in Home Healthcare.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Validation of Antineutrophil Cytoplasmic Antibody-Associated Vasculitis as the Cause of End-Stage Renal Disease in the US Renal Data System.ACR open rheumatology · 2022Article
- Big data and data processing in rheumatology: bioethical perspectives.Clinical rheumatology · 2020Review
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
objectivesClinical notes from electronic health records (EHR) are important to characterize the natural history, comorbidities, and complications of ANCA-associated vasculitis (AAV) because these details may not be captured by claims and structured data. However, labor-intensive chart review is often required to extract information from notes. We hypothesized that machine learning can automatically discover clinically-relevant themes across longitudinal notes to study AAV.
methodsThis retrospective study included prevalent PR3- or MPO-ANCA+ AAV cases managed within the Mass General Brigham integrated health care system with providers' notes available between March 1, 1990 and August 23, 2018. We generated clinically-relevant topics mentioned in notes using latent Dirichlet allocation-based topic modeling and conducted trend analyses of those topics over the 2 years prior to and 5 years after the initiation of AAV-specific treatment.
resultsThe study cohort included 660 patients with AAV. We generated 90 topics using 113,048 available notes. Topics were related to the AAV diagnosis, treatment, symptoms and manifestations (e.g., glomerulonephritis), and complications (e.g., end-stage renal disease, infection). AAV-related symptoms and psychiatric symptoms were mentioned months before treatment initiation. Topics related to pulmonary and renal diseases, diabetes, and infections were common during the disease course but followed distinct temporal patterns.
conclusionsAutomated topic modeling can be used to discover clinically-relevant themes and temporal patterns related to the diagnosis, treatment, comorbidities, and complications of AAV from EHR notes. Future research might compare the temporal patterns in a non-AAV cohort and leverage clinical notes to identify possible AAV cases prospectively.
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