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A Machine Learning Approach for Identifying People With Neuroinfectious Diseases in Electronic Health Records:

Arjun Singh^1,2, Shadi Sartipi^1,2, Haoqi Sun²

¹Department of Neurology, Massachusetts General Hospital, 55 Fruit St, Wang ACC 835, Boston, MA, 02114, United States, 1 2163379887.

JMIR Medical Informatics

|August 29, 2025

Summary

This summary is machine-generated.

A new natural language processing (NLP) model accurately identifies neuroinfectious diseases (NID) from clinical notes, outperforming traditional billing codes and other AI models. This machine learning approach offers a reliable tool for NID research and patient cohort identification.

Keywords:

EHR artificial intelligence clinical notes develop efficiency electronic health records expressions logistic regression machine learning neuroinfectious neuroinfectious disease neurology validate

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Area of Science:

Computational medicine
Medical informatics
Natural Language Processing (NLP)

Background:

Identifying neuroinfectious diseases (NID) using billing codes is imprecise, and manual chart reviews are labor-intensive.
Machine learning (ML) can analyze unstructured electronic health records (EHRs) for subtle NID indicators, improving efficiency and reducing misclassification.
Accurate NID classification is crucial for research and clinical decision support, yet using unstructured notes remains underexplored.

Purpose of the Study:

To develop and validate an ML model for identifying NIDs from unstructured patient notes.
To compare the NLP model's performance against ICD billing codes and a large language model (LLM).
To assess the model's generalizability across different healthcare institutions.

Main Methods:

An extreme gradient boosting (XGBoost) model was trained on 3000 clinical notes from Mass General Brigham (MGB).
Notes were processed using n-gram representations (n=1, 2, 3), with feature selection via L1 regularization.
Performance was evaluated using AUROC and AUPRC, with external validation on data from Beth Israel Deaconess Medical Center (BIDMC).

Main Results:

The NLP model achieved an AUROC of 0.98 and AUPRC of 0.89 on MGB test data, balancing specificity (0.96) and sensitivity (0.84).
ICD billing codes showed high sensitivity (0.97) but poor specificity (0.59), while Llama 3.2 had improved specificity (0.94) but low sensitivity (0.64).
The NLP model maintained strong performance on external BIDMC data (AUROC 0.98, AUPRC 0.78).

Conclusions:

The NLP model accurately identifies NID cases from clinical notes, demonstrating feasibility for large-scale NID research.
The model's performance across two independent hospital datasets highlights its potential for cohort generation.
Further external validation is recommended to enhance the generalizability of these findings to other institutions.