The use of natural language processing on pediatric diagnostic radiology reports in the electronic health record to identify deep venous thrombosis in children

Abstract

Venous thromboembolism (VTE) is a potentially life-threatening condition that includes both deep vein thrombosis (DVT) and pulmonary embolism. We sought to improve detection and reporting of children with a new diagnosis of VTE by applying natural language processing (NLP) tools to radiologists' reports. We validated an NLP tool, Reveal NLP (Health Fidelity Inc, San Mateo, CA) and inference rules engine's performance in identifying reports with deep venous thrombosis using a curated set of ultrasound reports. We then configured the NLP tool to scan all available radiology reports on a daily basis for studies that met criteria for VTE between July 1, 2015, and March 31, 2016. The NLP tool and inference rules engine correctly identified 140 out of 144 reports with positive DVT findings and 98 out of 106 negative reports in the validation set. The tool's sensitivity was 97.2% (95% CI 93-99.2%), specificity was 92.5% (95% CI 85.7-96.7%). Subsequently, the NLP tool and inference rules engine processed 6373 radiology reports from 3371 hospital encounters. The NLP tool and inference rules engine identified 178 positive reports and 3193 negative reports with a sensitivity of 82.9% (95% CI 74.8-89.2) and specificity of 97.5% (95% CI 96.9-98). The system functions well as a safety net to screen patients for HA-VTE on a daily basis and offers value as an automated, redundant system. To our knowledge, this is the first pediatric study to apply NLP technology in a prospective manner for HA-VTE identification.

Design and Implementation of a Comprehensive Surveillance System for Venous Thromboembolism in a Defined Region Using Electronic and Manual Approaches

“…Third, while electronic strategies will be critical to implement a VTE surveillance program, manual oversight will continue to be necessary to minimize errors in the data collected. Incorporation of a natural language processing strategy to review radiology reports for VTE, which has been described in several reports, [40][41][42][43] could be used to complement our strategy and decrease the number of records that need manual review. Last, linking imaging data to therapeutic interventions will be essential to assess the clinical impact of strategies introduced to prevent VTE, to monitor changes in incidence over time.…”

Natural Language Processing Performance for the Identification of Venous Thromboembolism in an Integrated Healthcare System

“…Venous thromboembolism (VTE), including deep vein thrombosis (DVT) and pulmonary embolism (PE) complicates surgical procedures, prolongs hospital stays, and when undiagnosed, increases mortality. 1,2 While the lifetime risk of VTE approximates 1:1000, VTE disproportionately affects the elderly, hospitalized medically ill and those with cancer. [3][4][5] The risk of VTE increases as much as 20-fold following surgery.…”

“…Methods of electronic medical record (EMR) interrogation using embedded computer algorithms for the identification of outcome events have been described. 1,2,[7][8][9] In a former study we reported the operating characteristics of an EMR-embedded algorithm that employed natural language processing (NLP) in our legacy electronic health record. 2 Natural language processing, also referred to as "text mining," 10 is programmed to interrogate free-text reports from different sources such as radiology reports, progress notes, and chart documentation to identify structured language that documents the diagnosis or findings of interest.…”

A Data-driven Framework for Learning and Visualizing Characteristics of Thrombotic Event Phenotypes from Clinical Texts

“…The Reveal NLP Engine, based on the MedLEE (Medical Language Extraction and Encoding Systems), extracts clinical terms using the Systematized Nomenclature of Medicine (SNOMED) terminology and applies inference rules to classify VTEs from radiology reports. 12 Reveal NLP has demonstrated high sensitivity (83%) and specificity (97%) when processing 6373 radiology reports from 3,371 hospital encounters.…”