Precision Clinical Medicine Through Machine Learning: Using High and Low Quantile Ranges of Vital Signs for Risk Stratification of ICU Patients-Reference-Cited by-同舟云学术

Precision Clinical Medicine Through Machine Learning: Using High and Low Quantile Ranges of Vital Signs for Risk Stratification of ICU Patients

Published:2022 Issue: Volume:10 Page:52418-52430
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Alghatani Khalid¹^ORCID,Ammar Nariman²^ORCID,Rezgui Abdelmounaam³,Shaban-Nejad Arash²^ORCID

Affiliation:

1. King Fahad Medical City, Riyadh, Saudi Arabia

2. Department of Pediatrics, College of Medicine, UTHSC-ORNL Center for Biomedical Informatics, The University of Tennessee Health Science Center, Memphis, TN, USA

3. School of Information Technology, Illinois State University, Normal, IL, USA

Funder

College of Applied Science and Technology (CAST), Illinois State University, Normal, IL, USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09775137.pdf?arnumber=9775137

Reference20 articles.

1. PhysioBank, PhysioToolkit, and PhysioNet

2. Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long short-term memory

3. Data mining using clinical physiology at discharge to predict ICU readmissions

4. Sociomarkers and biomarkers: predictive modeling in identifying pediatric asthma patients at risk of hospital revisits

5. DeepNote-GNN

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