TY - GEN
T1 - Automatic Extraction of Major Osteoporotic Fractures from Radiology Reports using Natural Language Processing
AU - Wang, Yanshan
AU - Mehrabi, Saeed
AU - Sohn, Sunghwan
AU - Atkinson, Elizabeth
AU - Amin, Shreyasee
AU - Liu, Hongfang
N1 - Funding Information:
V. ACKNOWLEDGEMENTS The authors would like to thank Marcia Erickson, R.N., Julie Gingras, R.N. and Joan LaPlante, R.N. for assistance with the fracture validation. This work was supported by research grants NIA P01AG04875, NIH R01GM102282 and NIH R01LM011934, and made possible by the Rochester Epidemiology Project (NIA R01AG034676), and the U.S. Public Health Service.
Publisher Copyright:
© 2018 IEEE.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/7/16
Y1 - 2018/7/16
N2 - 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.
AB - 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.
KW - fracture
KW - natural language processing
KW - osteoporosis
KW - radiology report
UR - http://www.scopus.com/inward/record.url?scp=85051022287&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85051022287&partnerID=8YFLogxK
U2 - 10.1109/ICHI-W.2018.00021
DO - 10.1109/ICHI-W.2018.00021
M3 - Conference contribution
AN - SCOPUS:85051022287
T3 - Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018
SP - 64
EP - 65
BT - Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018
Y2 - 4 June 2018 through 7 June 2018
ER -