Performance of a Natural Language Processing Method to Extract Stone Composition From the Electronic Health Record.

ABSTRACT:

Objectives

To demonstrate the utility of a natural language processing (NLP) algorithm for mining kidney stone composition in a large-scale electronic health records (EHR) repository.

Methods

We developed StoneX, a pattern-matching method for extracting kidney stone composition information from clinical notes. We trained the extraction algorithm on manually annotated text mentions of calcium oxalate monohydrate, calcium oxalate dihydrate, hydroxyapatite, brushite, uric acid, and struvite stones. We employed StoneX to identify patients with kidney stone composition data and mine >125 million notes from our institutional EHR. Analyses performed on the extracted patients included stone type conversions over time, survival analysis from a second stone surgery, and disease associ

SUBMITTER: Bejan CA 

PROVIDER: S-EPMC6778032 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature