Journal Article

Natural Language Processing in a Clinical Decision Support System for the Identification of Venous Thromboembolism: Algorithm Development and Validation

Tai, Mei-Hui, Zhang, Hui, Jin, Zhi-Geng, Yang, Ying, Guo, Yu-Tao, Yao, Yuan

2023

Overview

It remains unknown whether capturing data from electronic health records (EHRs) using natural language processing (NLP) can improve venous thromboembolism (VTE) detection in different clinical settings. The aim of this study was to validate the NLP algorithm in a clinical decision support system for VTE risk assessment and integrated care (DeVTEcare) to identify VTEs from EHRs. All inpatients aged ≥18 years in the Sixth Medical Center of the Chinese People's Liberation Army General Hospital from January 1 to December 31, 2021, were included as the validation cohort. The sensitivity, specificity, positive and negative likelihood ratios (LR+ and LR-, respectively), area under the receiver operating characteristic curve (AUC), and F1-scores along with their 95% CIs were used to analyze the performance of the NLP tool, with manual review of medical records as the reference standard for detecting deep vein thrombosis (DVT) and pulmonary embolism (PE). The primary end point was the performance of the NLP approach embedded into the EHR for VTE identification. The secondary end points were the performances to identify VTE among different hospital departments with different VTE risks. Subgroup analyses were performed among age, sex, and the study season. Among 30,152 patients (median age 56 [IQR 41-67] years; 14,247/30,152, 47.3% females), the prevalence of VTE, PE, and DVT was 2.1% (626/30,152), 0.6% (177/30,152), and 1.8% (532/30,152), respectively. The sensitivity, specificity, LR+, LR-, AUC, and F1-score of NLP-facilitated VTE detection were 89.9% (95% CI 87.3%-92.2%), 99.8% (95% CI 99.8%-99.9%), 483 (95% CI 370-629), 0.10 (95% CI 0.08-0.13), 0.95 (95% CI 0.94-0.96), and 0.90 (95% CI 0.90-0.91), respectively. Among departments of surgery, internal medicine, and intensive care units, the highest specificity (100% vs 99.7% vs 98.8%, respectively), LR+ (3202 vs 321 vs 77, respectively), and F1-score (0.95 vs 0.89 vs 0.92, respectively) were in the surgery department (all P<.001). Among low, intermediate, and high VTE risks in hospital departments, the low-risk department had the highest AUC (1.00 vs 0.94 vs 0.96, respectively) and F1-score (0.97 vs 0.90 vs 0.90, respectively) as well as the lowest LR- (0.00 vs 0.13 vs 0.08, respectively) (DeLong test for AUC; all P<.001). Subgroup analysis of the age, sex, and season demonstrated consistently good performance of VTE detection with >87% sensitivity and specificity and >89% AUC and F1-score. The NLP algorithm performed better among patients aged ≤65 years than among those aged >65 years (F1-score 0.93 vs 0.89, respectively; P<.001). The NLP algorithm in our DeVTEcare identified VTE well across different clinical settings, especially in patients in surgery units, departments with low-risk VTE, and patients aged ≤65 years. This algorithm can help to inform accurate in-hospital VTE rates and enhance risk-classified VTE integrated care in future research.

• Publisher: Gunther Eysenbach MD MPH, Associate Professor,JMIR Publications
• Subject: Adolescent / Adult / Age / Algorithms / Archives & records / Army / Asian cultural groups / Chinese languages / Clinical assessment / Clinical decision making / Computerized medical records / Data processing / Decision support systems / Decision Support Systems, Clinical / Deep vein thrombosis / Disease prevention / Electronic Health Records / Female / Generals / Health records / Hospitals / Humans / Identification / Inpatient care / Integrated care / Integrated delivery systems / Intensive care / Language / Male / Medical decision making / Medical records / Medicine / Middle Aged / Multimedia / Natural Language Processing / Original Paper / Patients / Pulmonary Embolism / Pulmonary embolisms / Risk Assessment / Sensors / Support networks / Surgery / Thromboembolism / Thrombosis / Tomography / Validity / Venous Thromboembolism - diagnosis / Venous thrombosis / Venous Thrombosis - diagnosis