title: Performance of a Machine Learning Classifier of Knee MRI Reports in Two Large Academic Radiology Practices: A Tool to Estimate Diagnostic Yield.
abstract: OBJECTIVE
  The purpose of this study is to evaluate the performance of a natural language processing (NLP) system in classifying a database of free-text knee MRI reports at two separate academic radiology practices.
  
  
  METHODS
  An NLP system that uses terms and patterns in manually classified narrative knee MRI reports was constructed. The NLP system was trained and tested on expert-classified knee MRI reports from two major health care organizations. Radiology reports were modeled in the training set as vectors, and a support vector machine framework was used to train the classifier. A separate test set from each organization was used to evaluate the performance of the system. We evaluated the performance of the system both within and across organizations. Standard evaluation metrics, such as accuracy, precision, recall, and F1 score (i.e., the weighted average of the precision and recall), and their respective 95% CIs were used to measure the efficacy of our classification system.
  
  
  RESULTS
  The accuracy for radiology reports that belonged to the model's clinically significant concept classes after training data from the same institution was good, yielding an F1 score greater than 90% (95% CI, 84.6-97.3%). Performance of the classifier on cross-institutional application without institution-specific training data yielded F1 scores of 77.6% (95% CI, 69.5-85.7%) and 90.2% (95% CI, 84.5-95.9%) at the two organizations studied.
  
  
  CONCLUSIONS
  The results show excellent accuracy by the NLP machine learning classifier in classifying free-text knee MRI reports, supporting the institution-independent reproducibility of knee MRI report classification. Furthermore, the machine learning classifier performed well on free-text knee MRI reports from another institution. These data support the feasibility of multiinstitutional classification of radiologic imaging text reports with a single machine learning classifier without requiring institution-specific training data.
authors.affiliation: 1 Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA.
authors.forename: Saeed
authors.initials: S
authors.lastname: Hassanpour
authors.affiliation: 2 Department of Radiology, Stanford University School of Medicine, Stanford University Medical Center, 725 Welch Rd, Rm 1675, Stanford, CA 94305-5913.
authors.forename: Curtis P
authors.initials: CP
authors.lastname: Langlotz
authors.affiliation: 3 Department of Radiology, Duke University Medical Center, Durham, NC.
authors.forename: Timothy J
authors.initials: TJ
authors.lastname: Amrhein
authors.affiliation: 3 Department of Radiology, Duke University Medical Center, Durham, NC.
authors.forename: Nicholas T
authors.initials: NT
authors.lastname: Befera
authors.affiliation: 2 Department of Radiology, Stanford University School of Medicine, Stanford University Medical Center, 725 Welch Rd, Rm 1675, Stanford, CA 94305-5913.
authors.forename: Matthew P
authors.initials: MP
authors.lastname: Lungren
mesh_terms: D000046:Academic Medical Centers / Q000706:statistics & numerical data*; D002140:California; D057225:Data Mining / Q000379:methods; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007717:Knee / Q000000981:diagnostic imaging*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods* / Q000706:statistics & numerical data*; D009323:Natural Language Processing; D009657:North Carolina; D010363:Pattern Recognition, Automated / Q000379:methods; D011872:Radiology Department, Hospital; D011873:Radiology Information Systems / Q000458:organization & administration / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D016526:Workload / Q000706:statistics & numerical data
keywords: MRI; classification; machine learning