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

PLoS One. 2020 Jul 1;15(7):e0214775. doi: 10.1371/journal.pone.0214775. eCollection 2020.


Background: 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.

Methods: 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.

Results: 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).

Conclusion: 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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Brain Injuries, Traumatic / diagnosis*
  • Brain Injuries, Traumatic / diagnostic imaging
  • Common Data Elements / standards*
  • Electronic Health Records
  • Humans
  • Tomography, X-Ray Computed

Grants and funding

This study was funded by the Minnesota Spinal Cord and Traumatic Brain Injury Research Fund (https://www.ohe.state.mn.us/mPg.cfm?pageID=2180) as well as the Rockswold Kaplan Endowed Chair for TBI Research (https://www.hennepinhealthcare.org/specialty/brain-injury-research/). US was awarded these funds and MM, DR, HC, and TA were paid at least in part by the funds. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.