“Yes, but will it work for my patients?” Driving clinically relevant research with benchmark datasets
Abstract Benchmark datasets have a powerful normative influence: by determining how the real world is represented in data, they define which problems will first be solved by algorithms built using the datasets and, by extension, who these algorithms will work for. It is desirable for these datasets...
Saved in:
| Main Authors: | Trishan Panch, Tom J. Pollard, Heather Mattie, Emily Lindemer, Pearse A. Keane, Leo Anthony Celi |
|---|---|
| Format: | article |
| Language: | EN |
| Published: |
Nature Portfolio
2020
|
| Subjects: | |
| Online Access: | https://doaj.org/article/137ec39e5c7443ce9fa5c7d8216b54ee |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Recalibration of deep learning models for abnormality detection in smartphone-captured chest radiograph
by: Po-Chih Kuo, et al.
Published: (2021) -
What do medical students actually need to know about artificial intelligence?
by: Liam G. McCoy, et al.
Published: (2020) -
Privacy protections to encourage use of health-relevant digital data in a learning health system
by: Deven McGraw, et al.
Published: (2021) -
Predicting critical state after COVID-19 diagnosis: model development using a large US electronic health record dataset
by: Mike D. Rinderknecht, et al.
Published: (2021) -
Patient-specific COVID-19 resource utilization prediction using fusion AI model
by: Amara Tariq, et al.
Published: (2021)