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

We developed a machine learning model to identify neuroinfectious diseases (NID) from electronic health record notes. Using 3,000 notes from Mass General Brigham, we trained an XGBoost model on text features extracted from clinical documentation. Our model achieved excellent performance with an AUROC of 0.977 and AUPRC of 0.894 on internal validation, significantly outperforming both ICD-code based identification (which showed high sensitivity of 97.1% but poor specificity of 59.1%) and zero-shot classification using LLaMA 3.2 (AUROC 0.80). The model maintained strong performance when tested on an external dataset of 600 notes from Beth Israel Deaconess Medical Center (AUROC 0.976, AUPRC 0.779). This approach provides an accurate, automated method for identifying patients with neuroinfectious diseases from clinical notes, enabling more precise cohort creation for research compared to traditional ICD code-based methods.