Enhancing early warning systems: predicting next vital signs using recurrent neural networks and attention models

[thumbnail of paper_fcc00ff5-73ce-4437-9573-7fbf03da67a5 (1).pdf]
Preview
Text - Accepted Version
· Please see our End User Agreement before downloading.
| Preview

Please see our End User Agreement.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Jehangir, B. and Li, W. (V.) orcid id iconORCID: https://orcid.org/0000-0003-2878-3185 (2024) Enhancing early warning systems: predicting next vital signs using recurrent neural networks and attention models. In: The 58th Hawaii International Conference on System Sciences, 7-10 Jan 2025, Hawaii, United States.

Item Type Conference or Workshop Item (Paper)
URI https://reading-clone.eprints-hosting.org/id/eprint/118732
Refereed Yes
Divisions Interdisciplinary centres and themes > Health Innovation Partnership (HIP)
Henley Business School > Digitalisation, Marketing and Entrepreneurship
Download/View statistics View download statistics for this item
[thumbnail of paper_fcc00ff5-73ce-4437-9573-7fbf03da67a5 (1).pdf]
Preview
Text - Accepted Version
· Please see our End User Agreement before downloading.
| Preview
Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Please see our End User Agreement.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

Downloads

Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Search Google Scholar