Using NLP to Extract Predicate History from Medical Device Approvals

Presenter Information

Yi Zhu, University of MinnesotaFollow
Alexander Everhart, University of MinnesotaFollow
Pinar Karaca Mandic, University of MinnesotaFollow
Soumya Sen, University of MinnesotaFollow

Paper Type

Short

Paper Number

1244

Description

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.

Recommended Citation

Zhu, Yi; Everhart, Alexander; Mandic, Pinar Karaca; and Sen, Soumya, "Using NLP to Extract Predicate History from Medical Device Approvals" (2020). ICIS 2020 Proceedings. 3.
https://aisel.aisnet.org/icis2020/is_health/is_health/3

Comments

5-Health