Paper Info
Title
Automatic Extraction of Major Osteoporotic Fractures from Radiology Reports using Natural Language Processing
Abstract
In this study, we developed a rule-based natural language processing (NLP) algorithm for automatic extraction of six major osteoporotic fractures from radiology reports. We validated the NLP algorithm using a dataset of radiology reports from Mayo Clinic with the gold standard constructed by medical experts. The micro-averaged sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score of the proposed NLP algorithm are 0.796, 0.978, 0.972, 0.831, 0.874, respectively. The highest F1-score was achieved at 0.958 for the extraction of proximal femur fracture while the lowest was 0.821 for the hand and finger/wrists fracture. The experimental results verified the effectiveness of the proposed rule-based NLP algorithm in the automatic extraction of major osteoporotic fractures from radiology reports.
Year
DOI
Venue
2018
10.1109/ICHI-W.2018.00021
2018 IEEE International Conference on Healthcare Informatics Workshop (ICHI-W)
Keywords
Field
DocType
natural language processing,radiology report,fracture,osteoporosis
Computer science,Femur,Prediction algorithms,Natural language processing,Artificial intelligence,Radiology,Gold standard,Radiology report
Conference
ISBN
Citations 
PageRank 
978-1-5386-6778-1
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Yanshan Wang14719.00
Saeed Mehrabi28015.55
Sunghwan Sohn368750.76
Elizabeth J. Atkinson4343.23
Shreyasee Amin5152.13
Hongfang Liu61479160.66