Report
Extraction and Analysis of Clinically Important Follow-up Recommendations in a Large Radiology Dataset
| العنوان: | Extraction and Analysis of Clinically Important Follow-up Recommendations in a Large Radiology Dataset |
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| المؤلفون: | Lau, Wilson, Payne, Thomas H, Uzuner, Ozlem, Yetisgen, Meliha |
| سنة النشر: | 2019 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language |
| الوصف: | Communication of follow-up recommendations when abnormalities are identified on imaging studies is prone to error. In this paper, we present a natural language processing approach based on deep learning to automatically identify clinically important recommendations in radiology reports. Our approach first identifies the recommendation sentences and then extracts reason, test, and time frame of the identified recommendations. To train our extraction models, we created a corpus of 567 radiology reports annotated for recommendation information. Our extraction models achieved 0.92 f-score for recommendation sentence, 0.65 f-score for reason, 0.73 f-score for test, and 0.84 f-score for time frame. We applied the extraction models to a set of over 3.3 million radiology reports and analyzed the adherence of follow-up recommendations. Comment: Under Review at American Medical Informatics Association Fall Symposium'2019 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1905.05877 |
| رقم الانضمام: | edsarx.1905.05877 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |