ArticleUrology2019
Performance of a Natural Language Processing Method to Extract Stone Composition From the Electronic Health Record.
Article in Urology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.
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Who cites it
5 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in urolithiasis: a systematic review of utilization and effectiveness.World journal of urology · 2024Pooled it
- The Ascent of Artificial Intelligence in Endourology: a Systematic Review Over the Last 2 Decades.Current urology reports · 2021Pooled it
- Large language models improve the identification of emergency department visits for symptomatic kidney stones.Scientific reports · 2025Article
- Use of Artificial Intelligence for Analyzing Kidney Stone Composition: Are We There Yet?Mayo Clinic proceedings. Digital health · 2023Article
- Theranostic roles of machine learning in clinical management of kidney stone disease.Computational and structural biotechnology journal · 2023Review
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Authors and funding
4 authors.
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Abstract
objectivesTo demonstrate the utility of a natural language processing (NLP) algorithm for mining kidney stone composition in a large-scale electronic health records (EHR) repository.
methodsWe 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 associations by stone composition to validate the phenotyping method against known associations.
resultsThe NLP algorithm identified 45,235 text mentions corresponding to 11,585 patients. Overall, the system achieved positive predictive value >90% for calcium oxalate monohydrate, calcium oxalate dihydrate, hydroxyapatite, brushite, and struvite; except for uric acid (positive predictive value = 87.5%). Survival analysis from a second stone surgery showed statistically significant differences among stone types (P = .03). Several phenotype associations were found: uric acid-type 2 diabetes (odds ratio, OR = 2.69, 95% confidence intervals, CI = 1.91-3.79), struvite-neurogenic bladder (OR = 12.27, 95% CI = 4.33-34.79), struvite-urinary tract infection (OR = 7.36, 95% CI = 3.01-17.99), hydroxyapatite-pulmonary collapse (OR = 3.67, 95% CI = 2.10-6.42), hydroxyapatite-neurogenic bladder (OR = 5.23, 95% CI = 2.05-13.36), brushite-calcium metabolism disorder (OR = 4.59, 95% CI = 2.14-9.81), and brushite-hypercalcemia (OR = 4.09, 95% CI = 1.90-8.80).
conclusionNLP extraction of kidney stone composition from large-scale EHRs is feasible with high precision, enabling high-throughput epidemiological studies of kidney stone disease. These tools will enable high fidelity kidney stone research from the EHR.
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