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This paper is in the following e-collection/theme issue:

Natural Language Processing (954) Clinical Informatics (1444) Clinical Information and Decision Making (2070) Electronic Health Records (1378) Machine Learning (2229) Artificial Intelligence (2347) Tools, Programs and Algorithms (408) Neurology and Neurosciences (302) Infectious Diseases (non-STD/STI) (1757) Ontologies, Classifications, and Coding (347)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/63157, first published .
A Machine Learning Approach for Identifying People With Neuroinfectious Diseases in Electronic Health Records: Algorithm Development and Validation

A Machine Learning Approach for Identifying People With Neuroinfectious Diseases in Electronic Health Records: Algorithm Development and Validation

A Machine Learning Approach for Identifying People With Neuroinfectious Diseases in Electronic Health Records: Algorithm Development and Validation

Authors of this article:

Arjun Singh1, 2 Author Orcid Image ;   Shadi Sartipi1, 2 Author Orcid Image ;   Haoqi Sun2 Author Orcid Image ;   Rebecca Milde3 Author Orcid Image ;   Niels Turley3 Author Orcid Image ;   Carson Quinn1, 4 Author Orcid Image ;   G Kyle Harrold1, 2, 4 Author Orcid Image ;   Rebecca L Gillani1 Author Orcid Image ;   Sarah E Turbett5, 6 Author Orcid Image ;   Sudeshna Das1, 2 Author Orcid Image ;   Sahar Zafar1, 2 Author Orcid Image ;   Marta Fernandes1, 2 Author Orcid Image ;   M Brandon Westover1, 2, 3 Author Orcid Image ;   Shibani S Mukerji1, 2 Author Orcid Image
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Journals

  1. Ling S, Wang L, Chung K, Tsai W, Chen Y, Chen H, Hsu H, Shi H. Predictive machine learning algorithms for depression and anxiety disorders in six cancer types: a comprehensive multi-center population-based study. Journal of the Formosan Medical Association 2026 View