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J Thromb Thrombolysis. 2017 Oct;44(3):281-290. doi: 10.1007/s11239-017-1532-y.

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

Author information

1
Section of Biomedical Informatics, Department of Anesthesiology & Critical Care Medicine, The Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, 3401 Civic Center Blvd, Suite 9329, Philadelphia, PA, 19096, USA. galvezj@email.chop.edu.
2
Clinical Data & Analytics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
3
Enterprise Analytics and Reporting, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
4
Section of Biomedical Informatics, Department of Anesthesiology & Critical Care Medicine, The Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, 3401 Civic Center Blvd, Suite 9329, Philadelphia, PA, 19096, USA.
5
Hemostasis and Thrombosis Center, Division of Hematology, The Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.

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.

KEYWORDS:

Epidemiology; Natural language processing; Pediatrics; Quality improvement; Venous thromboembolism; Venous thrombosis

PMID:
28815363
DOI:
10.1007/s11239-017-1532-y
[Indexed for MEDLINE]

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