Dataset Information

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 associations by stone composition to validate the phenotyping method against known associations. RESULTS:The 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). CONCLUSION:NLP 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.

SUBMITTER: Bejan CA

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

REPOSITORIES: biostudies-literature

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

Bejan Cosmin A CA Lee Daniel J DJ Xu Yaomin Y Hsi Ryan S RS

Urology 20190713

<h4>Objectives</h4>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.<h4>Methods</h4>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 s ...[more]

PMID: 31310771

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Project description:ImportanceAccurate prediction of outcomes among patients in intensive care units (ICUs) is important for clinical research and monitoring care quality. Most existing prediction models do not take full advantage of the electronic health record, using only the single worst value of laboratory tests and vital signs and largely ignoring information present in free-text notes. Whether capturing more of the available data and applying machine learning and natural language processing (NLP) can improve and automate the prediction of outcomes among patients in the ICU remains unknown.ObjectivesTo evaluate the change in power for a mortality prediction model among patients in the ICU achieved by incorporating measures of clinical trajectory together with NLP of clinical text and to assess the generalizability of this approach.Design, setting, and participantsThis retrospective cohort study included 101 196 patients with a first-time admission to the ICU and a length of stay of at least 4 hours. Twenty ICUs at 2 academic medical centers (University of California, San Francisco [UCSF], and Beth Israel Deaconess Medical Center [BIDMC], Boston, Massachusetts) and 1 community hospital (Mills-Peninsula Medical Center [MPMC], Burlingame, California) contributed data from January 1, 2001, through June 1, 2017. Data were analyzed from July 1, 2017, through August 1, 2018.Main outcomes and measuresIn-hospital mortality and model discrimination as assessed by the area under the receiver operating characteristic curve (AUC) and model calibration as assessed by the modified Hosmer-Lemeshow statistic.ResultsAmong 101 196 patients included in the analysis, 51.3% (n = 51 899) were male, with a mean (SD) age of 61.3 (17.1) years; their in-hospital mortality rate was 10.4% (n = 10 505). A baseline model using only the highest and lowest observed values for each laboratory test result or vital sign achieved a cross-validated AUC of 0.831 (95% CI, 0.830-0.832). In contrast, that model augmented with measures of clinical trajectory achieved an AUC of 0.899 (95% CI, 0.896-0.902; P < .001 for AUC difference). Further augmenting this model with NLP-derived terms associated with mortality further increased the AUC to 0.922 (95% CI, 0.916-0.924; P < .001). These NLP-derived terms were associated with improved model performance even when applied across sites (AUC difference for UCSF: 0.077 to 0.021; AUC difference for MPMC: 0.071 to 0.051; AUC difference for BIDMC: 0.035 to 0.043; P < .001) when augmenting with NLP at each site.Conclusions and relevanceIntensive care unit mortality prediction models incorporating measures of clinical trajectory and NLP-derived terms yielded excellent predictive performance and generalized well in this sample of hospitals. The role of these automated algorithms, particularly those using unstructured data from notes and other sources, in clinical research and quality improvement seems to merit additional investigation.

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Dataset Information

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

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

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