  Systemic lupus erythematosus (SLE) is a rare autoimmune disorder
characterized by an unpredictable course of flares and remission with diverse
manifestations. Lupus nephritis, one of the major disease manifestations of SLE
for organ damage and mortality, is a key component of lupus classification
criteria. Accurately identifying lupus nephritis in electronic health records
(EHRs) would therefore benefit large cohort observational studies and clinical
trials where characterization of the patient population is critical for
recruitment, study design, and analysis. Lupus nephritis can be recognized
through procedure codes and structured data, such as laboratory tests. However,
other critical information documenting lupus nephritis, such as histologic
reports from kidney biopsies and prior medical history narratives, require
sophisticated text processing to mine information from pathology reports and
clinical notes. In this study, we developed algorithms to identify lupus
nephritis with and without natural language processing (NLP) using EHR data. We
developed four algorithms: a rule-based algorithm using only structured data
(baseline algorithm) and three algorithms using different NLP models. The three
NLP models are based on regularized logistic regression and use different sets
of features including positive mention of concept unique identifiers (CUIs),
number of appearances of CUIs, and a mixture of three components respectively.
The baseline algorithm and the best performed NLP algorithm were external
validated on a dataset from Vanderbilt University Medical Center (VUMC). Our
best performing NLP model incorporating features from both structured data,
regular expression concepts, and mapped CUIs improved F measure in both the
NMEDW (0.41 vs 0.79) and VUMC (0.62 vs 0.96) datasets compared to the baseline
lupus nephritis algorithm.

Machine Learning