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      Automated Outcome Classification of Computed Tomography Imaging Reports for Pediatric Traumatic Brain Injury

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          Abstract

          Background

          The authors have previously demonstrated highly reliable automated classification of free text computed tomography (CT) imaging reports using a hybrid system that pairs linguistic (natural language processing) and statistical (machine learning) techniques. Previously performed for identifying the outcome of orbital fracture in unprocessed radiology reports from a clinical data repository, the performance has not been replicated for more complex outcomes.

          Objectives

          To validate automated outcome classification performance of a hybrid natural language processing (NLP) and machine learning system for brain CT imaging reports. The hypothesis was that our system has performance characteristics for identifying pediatric traumatic brain injury (TBI).

          Methods

          This was a secondary analysis of a subset of 2,121 CT reports from the Pediatric Emergency Care Applied Research Network (PECARN) TBI study. For that project, radiologists dictated CT reports as free text, which were then de-identified and scanned as PDF documents. Trained data abstractors manually coded each report for TBI outcome. Text was extracted from the PDF files using optical character recognition. The dataset was randomly split evenly for training and testing. Training patient reports were used as input to the Medical Language Extraction and Encoding (MedLEE) NLP tool to create structured output containing standardized medical terms and modifiers for negation, certainty, and temporal status. A random subset stratified by site was analyzed using descriptive quantitative content analysis to confirm identification of TBI findings based upon the National Institute of Neurological Disorders and Stroke Common Data Elements project. Findings were coded for presence or absence, weighted by frequency of mentions, and past/future/indication modifiers were filtered. After combining with the manual reference standard, a decision tree classifier was created using data mining tools WEKA 3.7.5 and Salford Predictive Miner 7.0. Performance of the decision tree classifier was evaluated on the test patient reports.

          Results

          The prevalence of TBI in the sampled population was 159 out of 2,217 (7.2%). The automated classification for pediatric TBI is comparable to our prior results, with the notable exception of lower positive predictive value (PPV). Manual review of misclassified reports, 95.5% of which were false positives, revealed that a sizable number of false-positive errors were due to differing outcome definitions between NINDS TBI findings and PECARN clinical important TBI findings, and report ambiguity not meeting definition criteria.

          Conclusions

          A hybrid NLP and machine learning automated classification system continues to show promise in coding free-text electronic clinical data. For complex outcomes, it can reliably identify negative reports, but manual review of positive reports may be required. As such, it can still streamline data collection for clinical research and performance improvement.

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          Author and article information

          Journal
          9418450
          20159
          Acad Emerg Med
          Acad Emerg Med
          Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
          1069-6563
          1553-2712
          23 January 2016
          14 January 2016
          February 2016
          06 March 2017
          : 23
          : 2
          : 171-178
          Affiliations
          Department of Emergency Medicine, Harbor-UCLA Medical Center, (KY) Torrance, CA; Computer Science Department, Portland State University, (ES) Portland, OR; Computer Science Department, The George Washington University, (HC)Washington, DC; Howard University School of Medicine, (WBC) Washington, DC; Children’s Research Institute, Children's National Health System, (PSH) Washington, DC; Division of Emergency Medicine, Children's National Health System, (JMC) Washington, DC
          Author notes
          Contact Author. Kabir Yadav, MDCM, MS, MSHS, Department of Emergency Medicine, Harbor-UCLA Medical Center, (KY) Torrance, CA, kabir@ 123456emedharbor.edu , Telephone: 310-222-6886; Fax: 310-212-6101
          Article
          PMC5338693 PMC5338693 5338693 nihpa744738
          10.1111/acem.12859
          5338693
          26766600
          fcd6a4ff-175b-41ec-beb6-b38b7c22392b
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