Natural language processing for automated quantification of brain metastases reported in free-text radiology reports
EANS Academy. Senders J. 09/27/19; 276113; EP12065
Mr. Joeky T. Senders
Mr. Joeky T. Senders

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Introduction: Although the bulk of patient-generated health data is increasing exponentially, its utilization is impeded because most data comes in unstructured format, namely free-text clinical reports. A variety of natural language processing (NLP) methods have emerged to automate the processing of free text ranging from statistical to deep learning-based models; however, the optimal approach for medical text analysis remains to be determined. The aim of this study was to provide a head-to-head comparison of novel NLP techniques and inform future studies about their utility for automated medical text analysis.
Methods: Magnetic resonance imaging reports of patients with brain metastases treated in two tertiary centers were retrieved and manually annotated using a binary classification (single metastasis versus two or more metastases). Multiple bag-of-words and sequence-based NLP models were developed and compared after randomly splitting the annotated reports into a training and test set in an 80:20 ratio.
Results: A total of 1479 radiology reports of patients diagnosed with brain metastases were retrieved. The LASSO regression model demonstrated the best overall performance on the hold-out test set with an area under the receiver operating curve of 0.92 (95%CI 0.89-0.94), accuracy of 83% (95%CI 80-87%), calibration intercept of -0.06 (95%CI -0.14-0.01), and calibration slope of 1.06 (95%CI 0.95-1.17).
Conclusion: Among various NLP techniques, the bag-of-words approach combined with a LASSO regression model demonstrated the best overall performance in extracting binary outcomes from free-text clinical reports. This study provides a framework for the development of machine learning-based NLP models, as well as a clinical vignette in patients diagnosed with brain metastasis.
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