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Automated Monkeypox Identification from Electronic Medical Records Using Large Language Models - Shenzhen City,

Diyang Xue¹, Ye Ye², Yu Wu¹

¹Shenzhen Center for Disease Control and Prevention, Shenzhen City, Guangdong Province, China.

China CDC Weekly

|May 25, 2026

Summary

Large language models (LLMs) can automatically identify monkeypox (mpox) cases from electronic medical records. This approach using DeepSeek-R1-14B achieved high accuracy, outperforming traditional methods for early mpox detection.

Keywords:

DeepSeek Electronic Medical Records Machine Learning Surveillance mpox

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Area of Science:

Infectious Diseases
Artificial Intelligence
Medical Informatics

Background:

Monkeypox (mpox) presents with diverse symptoms, complicating early identification.
Traditional diagnostic methods may cause delays in mpox detection.
Electronic medical record (EMR) free-text data is underutilized for mpox surveillance.

Purpose of the Study:

To explore automated mpox identification using large language models (LLMs) on EMR data.
To evaluate the performance of LLM-based feature extraction against traditional methods.

Main Methods:

Retrospective study of 239 individuals (126 mpox cases, 113 controls).
Used DeepSeek-R1-14B LLM to extract clinical features from EMR chief complaints and histories.
Trained Naïve Bayes, logistic regression, and random forest classifiers; compared with TF-IDF baseline and Qwen3-14B.

Main Results:

LLM-based features (DeepSeek) outperformed TF-IDF and Qwen3-14B.
Logistic regression with DeepSeek features achieved AUROC of 0.927 and accuracy of 87.5%.
DeepSeek accurately extracted key mpox symptoms (fever, rash, pustules) with 96.1% accuracy.

Conclusions:

LLM-based clinical feature extraction is a promising method for automated mpox identification.
This approach can support intelligent surveillance and early warning systems for mpox.