Multitask Deep Learning for Cost-Effective Prediction of Patient's Length of Stay and Readmission State Using Multimodal Physical Activity Sensory Data-Reference-Cited by-同舟云学术

Multitask Deep Learning for Cost-Effective Prediction of Patient's Length of Stay and Readmission State Using Multimodal Physical Activity Sensory Data

Published:2022-12 Issue:12 Volume:26 Page:5793-5804
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Ali Sajid¹,El-Sappagh Shaker²,Ali Farman³^ORCID,Imran Muhammad⁴^ORCID,Abuhmed Tamer²^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon-si, South Korea

2. College of Computing and Informatics, Sungkyunkwan University, Suwon-si, South Korea

3. Department of Software, Sejong University, Seoul, South Korea

4. Institute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, VIC, Australia

Funder

Ministry of Science and ICT, Korea

Institute for Information and Communications Technology Planning and Evaluation

National Research Foundation of Korea

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

http://xplorestaging.ieee.org/ielx7/6221020/9973412/09869298.pdf?arnumber=9869298

Reference59 articles.

1. Modelling hospital readmissions under frailty conditions for healthy aging

2. Multi-task cox proportional hazard model for predicting risk of unplanned hospital readmission

3. Accelerometer-Based Human Activity Recognition for Patient Monitoring Using a Deep Neural Network

4. Estimation of Tidal Volume Using Load Cells on a Hospital Bed

5. Modelling healthcare systems with phase-type distributions

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