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Development of an automated system for querying radiology reports and recording deep venous thromboses and pulmonary emboli
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Narain, Wazim R.. Development of an automated system for querying radiology reports and recording deep venous thromboses and pulmonary emboli. Retrieved from https://doi.org/doi:10.7282/T3T155RQ

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TitleDevelopment of an automated system for querying radiology reports and recording deep venous thromboses and pulmonary emboli
NameNarain, Wazim R. (author); Srinivasan, Shankar (chair); Coffman, Frederick (internal member); Stetson, Pete (outside member); Rutgers University; School of Health Professions
Date Created2016
Other Date2016-05 (degree)
SubjectBiomedical Informatics, Pulmonary embolism, Thrombosis
Extent1 online resource (79 p. : ill.)
DescriptionAs the United States healthcare system transitions to a pay for performance model in response to increasing costs and utilization, assessing quality of care has come to the forefront. Venous thromboembolisms (VTE), which include deep vein thrombosis (DVT) and pulmonary embolism (PE), is a key measure of quality of hospital care and are associated with increased morbidity, mortality and cost in hospitalized patients. Traditional ways of measuring quality and identifying adverse events such as VTE using administrative data are convenient but lack accuracy. Manual review of clinical records is widely considered the gold standard but resource intensive. Consequently, this study sought to determine the accuracy of Natural Language Processing (NLP) and machine learning classifiers in identifying VTE from free text data. This study used radiology reports performed within 30 days of surgery for hospital patients sampled from 2011 through 2014 as part of the American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP). Though records for this sample were previously reviewed and VTE cases identified, a total of 909 ultrasound reports and 1,837 computed tomography (CT) angiogram reports were again manually reviewed to identify DVT/PE within each report and served as the gold standard. The Naïve Bayes, k-Nearest Neighbors (kNN), C4.5 decision tree, and support vector machine (SVM) classifiers were trained on 70% of the total preprocessed reports and performance was assessed on the remaining 30%. DVTs were identified in 16.8% of all ultrasound reports and PEs were identified in 5.0% of all CT angiogram reports. SVM yielded the best results in classifying both DVT and PE, with precision of 91.3%, recall of 95.5% and F-measure of 93.3% for DVT classification and precision of 93.1%, recall of 87.1% and F-measure of 90.0% for PE classification. In conclusion, NLP along with statistical machine learning classifiers can accurately identify VTE from narrative radiology reports.
NotePh.D.
NoteIncludes bibliographical references
Noteby Wazim R. Narain
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T3T155RQ
Languageeng
CollectionSchool of Health Professions ETD Collection
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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