Machine Learning Electronic Health Record Identification of Patients with Rheumatoid Arthritis: Algorithm Pipeline Development and Validation Study

Maarseveen TD, Meinderink T, Reinders MJT, Knitza J, Huizinga TWJ, Kleyer A, Simon D, Van Den Akker EB, Knevel R (2020)

Publication Type: Journal article

Publication year: 2020

Journal

JMIR Medical Informatics JMIR Publications

Book Volume: 8

Journal Issue: 11

DOI: 10.2196/23930

Abstract

Background: Financial codes are often used to extract diagnoses from electronic health records. This approach is prone to false positives. Alternatively, queries are constructed, but these are highly center and language specific. A tantalizing alternative is the automatic identification of patients by employing machine learning on format-free text entries.

Authors with CRIS profile

Timo Meinderink Department of Medicine 3 – Rheumatology and Immunology Johannes Knitza Department of Medicine 3 – Rheumatology and Immunology Arnd Kleyer Lehrstuhl für Innere Medizin III David Simon Lehrstuhl für Innere Medizin III

Involved external institutions

Leiden University NL Netherlands (NL)

How to cite

APA:

Maarseveen, T.D., Meinderink, T., Reinders, M.J.T., Knitza, J., Huizinga, T.W.J., Kleyer, A.,... Knevel, R. (2020). Machine Learning Electronic Health Record Identification of Patients with Rheumatoid Arthritis: Algorithm Pipeline Development and Validation Study. JMIR Medical Informatics, 8(11). https://doi.org/10.2196/23930

MLA:

Maarseveen, Tjardo D., et al. "Machine Learning Electronic Health Record Identification of Patients with Rheumatoid Arthritis: Algorithm Pipeline Development and Validation Study." JMIR Medical Informatics 8.11 (2020).

BibTeX: Download