Tools
Tools
Zebin, Tahmina 
ORCID: https://orcid.org/0000-0003-0437-0570, Rezvy, Shahadate and Chaussalet, Thierry
(2019)
A deep learning approach for length of stay prediction in clinical settings from medical records.
In: 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, 2019-07-09 - 2019-07-11, Certosa di Pontignano, Siena.
Preview |
PDF (Author_final_LOS)
- Accepted Version
Download (416kB) | Preview |
Abstract
Deep neural networks are becoming an increasingly popularsolution for predictive modeling using electronic health records because of their capability of learning complex patterns and behaviors from large volumes of patient records. In this paper, we have applied an autoencoded deep neural network algorithm aimed at identifying short(0-7 days) and long stays (> 7 days) in hospital based on patient admission records, demographics, diagnosis codes and chart events. We validated our approach using the de-identified MIMIC-III dataset. This proposed Autoencoder+DNN model shows that the two classes are separable with 73.2% accuracy based upon ICD-9 and demographics features. Once vital chart events data such as body temperature, blood pressure, heart rate information available after 24 hour of admission is added to the model, the classification accuracy is increased up to 77.7%. Our results showed a better performance when compared to a baseline random forest model.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | deep learning,prediction modelling,length of stay,electronic health records |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Smart Emerging Technologies |
| Depositing User: | LivePure Connector |
| Date Deposited: | 27 Nov 2019 01:23 |
| Last Modified: | 05 May 2024 05:30 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/73012 |
| DOI: | 10.1109/CIBCB.2019.8791477 |
Downloads
Downloads per month over past year
Actions (login required)
 |
View Item |