Exploring the Preprocessing of a Time-Series Healthcare Administrative Dataset on Deep Learning to Improve Prediction
Preprocessing methods are important in enhancing prediction performance for time-series administrative data. This study underscores the importance of preprocessing methods by comparing two data representation techniques, that can be used with sliding window techniques for time-series administrative...
Saved in:
Main Authors: | Faezehsadat Shahidi, M. Ethan Macdonald, Dallas Seitz, Rebecca Barry, Geoffrey Messier |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10807221/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
LSTM-based framework for predicting point defect percentage in semiconductor materials using simulated XRD patterns
by: Mehran Motamedi, et al.
Published: (2024-10-01) -
Differentiated and negotiable mechanism for data communication
by: Wenlong KOU, et al.
Published: (2021-10-01) -
Windows Server 2019 administration fundamentals : a beginner's guide to managing and administering windows server environments /
by: Dauti, Bekim
Published: (2019) -
Influence of Water Regulation on Runoff in Dongjiang River Basin
by: ZHUANG Shengjie, et al.
Published: (2024-11-01) -
Guaranteeing service availability in cloud computing
by: Shi-jun SHEN, et al.
Published: (2014-02-01)