Identification of an ANCA-Associated Vasculitis Cohort Using Deep Learning and Electronic Health Records

Abstract

Background ANCA-associated vasculitis (AAV) is a rare but serious disease. Traditional case-identification methods using claims data can be time-intensive and may miss important subgroups. We hypothesized that a deep learning model analyzing electronic health records (EHR) can more accurately identify AAV cases. Methods We examined the Mass General Brigham (MGB) repository of clinical documentation from 12/1/1979 to 5/11/2021, using expert-curated keywords and ICD codes to identify a large cohort of potential AAV cases. Three labeled datasets (I, II, III) were created, each containing note sections. We trained and evaluated a range of machine learning and deep learning algorithms for note-level classification, using metrics like positive predictive value (PPV), sensitivity, F-score, area under the receiver operating characteristic curve (AUROC), and area under the precision and recall curve (AUPRC). The hierarchical attention network (HAN) was further evaluated for its ability to classify AAV cases at the patient-level, compared with rule-based algorithms in 2000 randomly chosen samples. Results Datasets I, II, and III comprised 6000, 3008, and 7500 note sections, respectively. HAN achieved the highest AUROC in all three datasets, with scores of 0.983, 0.991, and 0.991. The deep learning approach also had among the highest PPVs across the three datasets (0.941, 0.954, and 0.800, respectively). In a test cohort of 2000 cases, the HAN model achieved a PPV of 0.262 and an estimated sensitivity of 0.975. Compared to the best rule-based algorithm, HAN identified six additional AAV cases, representing 13% of the total. Conclusion The deep learning model effectively classifies clinical note sections for AAV diagnosis. Its application to EHR notes can potentially uncover additional cases missed by traditional rule-based methods.

Contribution highlights

• Background ANCA-associated vasculitis (AAV) is a rare but serious disease.
• Traditional case-identification methods using claims data can be time-intensive and may miss important subgroups.

Extracted from the public abstract; no claims have been added.

Code and data

Publication metadata

DOI

10.1016/j.ijmedinf.2025.105797

PMID

39864108

Volume

196

Pages / article

105797

Version relationship

No separate preprint-to-publication relationship is currently recorded.

BibTeX

@article{Wang2025Identificationanca,
  title = {Identification of an ANCA-Associated Vasculitis Cohort Using Deep Learning and Electronic Health Records},
  author = {Liqin Wang and John Novoa-Laurentiev and Claire Cook and Shruthi Srivatsan and Yining Hua and Jie Yang and Eli Miloslavsky and Hyon K Choi and Li Zhou and Zachary S Wallace},
  journal = {International Journal of Medical Informatics},
  year = {2025},
  url = {https://www.sciencedirect.com/science/article/pii/S1386505624003551},
  doi = {10.1016/j.ijmedinf.2025.105797},
  pmid = {39864108},
  volume = {196},
  pages = {105797}
}