Augmentation Techniques for Sequential Clinical Data to Improve Deep Learning Prediction Techniques-Reference-Cited by-同舟云学术

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Augmentation Techniques for Sequential Clinical Data to Improve Deep Learning Prediction Techniques

Published:2020-07 Issue: Volume: Page:
ISSN:
Container-title:2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)
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Author:

Choquenaira Florez Alexander Ylnner,Scabora Lucas,Amer-Yahia Sihem,Rodrigues Junior Jose Fernando

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9169740/9182790/09183365.pdf?arnumber=9183365

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Framework for Ranking Machine Learning Predictions of Limited, Multimodal, and Longitudinal Behavioral Passive Sensing Data: Combining User-Agnostic and Personalized Modeling;JMIR AI;2024-05-20

2. Enhancing Predictive Power in Personalized Breast Cancer Treatment through the Integration of Multi-Omics Data and Clinical Information with Deep Learning Models;2024 MIT Art, Design and Technology School of Computing International Conference (MITADTSoCiCon);2024-04-25

3. Evaluating the Potential and Realized Impact of Data Augmentations;2023 IEEE Symposium Series on Computational Intelligence (SSCI);2023-12-05

4. Framework for Ranking Machine Learning Predictions of Limited, Multimodal, and Longitudinal Behavioral Passive Sensing Data: Combining User-Agnostic and Personalized Modeling (Preprint);2023-04-02

5. AttentionHCare: Advances on computer-aided medical prognosis using attention-based neural networks;2022 International Joint Conference on Neural Networks (IJCNN);2022-07-18

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