tbiExtractor: A framework for extracting traumatic brain injury common data elements from radiology reports

• Published in: PloS one Vol. 15; no. 7; p. e0214775
• Main Authors: Mahan, Margaret, Rafter, Daniel, Casey, Hannah, Engelking, Marta, Abdallah, Tessneem, Truwit, Charles, Oswood, Mark, Samadani, Uzma
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.07.2020; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Annotations; Automation; Biology; Biology and Life Sciences; Brain; Brain injuries; Brain Injuries, Traumatic - diagnosis; Brain Injuries, Traumatic - diagnostic imaging; CAT scans; Clinical trials; Common Data Elements - standards; Computed tomography; Computer and Information Sciences; Consent; Consistency; Data mining; Datasets; Diagnosis; Electronic Health Records; Electronic medical records; Feature extraction; Head injuries; Health aspects; Humans; Informatics; Medical records; Medicine and Health Sciences; Methods; Natural language processing; Pathology; Physical Sciences; Radiology; Research and Analysis Methods; Terminology; Tomography, X-Ray Computed; Traumatic brain injury; User interface; United States; United States > US; Minnesota
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0214775

The manual extraction of valuable data from electronic medical records is cumbersome, error-prone, and inconsistent. By automating extraction in conjunction with standardized terminology, the quality and consistency of data utilized for research and clinical purposes would be substantially improved. Here, we set out to develop and validate a framework to extract pertinent clinical conditions for traumatic brain injury (TBI) from computed tomography (CT) reports. We developed tbiExtractor, which extends pyConTextNLP, a regular expression algorithm using negation detection and contextual features, to create a framework for extracting TBI common data elements from radiology reports. The algorithm inputs radiology reports and outputs a structured summary containing 27 clinical findings with their respective annotations. Development and validation of the algorithm was completed using two physician annotators as the gold standard. tbiExtractor displayed high sensitivity (0.92-0.94) and specificity (0.99) when compared to the gold standard. The algorithm also demonstrated a high equivalence (94.6%) with the annotators. A majority of clinical findings (85%) had minimal errors (F1 Score ≥ 0.80). When compared to annotators, tbiExtractor extracted information in significantly less time (0.3 sec vs 1.7 min per report). tbiExtractor is a validated algorithm for extraction of TBI common data elements from radiology reports. This automation reduces the time spent to extract structured data and improves the consistency of data extracted. Lastly, tbiExtractor can be used to stratify subjects into groups based on visible damage by partitioning the annotations of the pertinent clinical conditions on a radiology report.