TY - GEN
T1 - Using NLP to extract predicate history from medical device approvals
AU - Zhu, Yi
AU - Everhart, Alexander
AU - Karaca-Mandic, Pinar
AU - Sen, Soumya
N1 - Publisher Copyright:
© ICIS 2020. All rights reserved.
PY - 2021
Y1 - 2021
N2 - The FDA approves new moderate-risk medical devices through the Pre-Market Notification (510(k)) process based on their similarity to previously cleared devices known as “predicates”. It is unknown how the features of predicates are associated with the safety of new devices. To address this issue, we employ Natural Language Processing (NLP) techniques to extract the complete list of predicates for each new device from their 510(k) documents and create a predicate database, based on which we assess the association between features of predicates and the likelihood of new devices' recalls. The results help answer questions such as whether new devices with longer predicate history chain are more likely to be recalled and whether new devices with more predicates are more likely to be recalled. Our proposed data-driven approach for analyzing the role of predicates in the 510(k) process helps researchers explore how the process promotes the development of safe medical devices.
AB - The FDA approves new moderate-risk medical devices through the Pre-Market Notification (510(k)) process based on their similarity to previously cleared devices known as “predicates”. It is unknown how the features of predicates are associated with the safety of new devices. To address this issue, we employ Natural Language Processing (NLP) techniques to extract the complete list of predicates for each new device from their 510(k) documents and create a predicate database, based on which we assess the association between features of predicates and the likelihood of new devices' recalls. The results help answer questions such as whether new devices with longer predicate history chain are more likely to be recalled and whether new devices with more predicates are more likely to be recalled. Our proposed data-driven approach for analyzing the role of predicates in the 510(k) process helps researchers explore how the process promotes the development of safe medical devices.
KW - Medical device
KW - Natural language processing
KW - Predicate device
KW - Product recall
UR - http://www.scopus.com/inward/record.url?scp=85103453410&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85103453410
T3 - International Conference on Information Systems, ICIS 2020 - Making Digital Inclusive: Blending the Local and the Global
BT - International Conference on Information Systems, ICIS 2020 - Making Digital Inclusive
PB - Association for Information Systems
T2 - 2020 International Conference on Information Systems - Making Digital Inclusive: Blending the Local and the Global, ICIS 2020
Y2 - 13 December 2020 through 16 December 2020
ER -