Computational simulation of virtual patients reduces dataset bias and improves machine learning-based detection of ARDS from noisy heterogeneous ICU datasets-Reference-Cited by-同舟云学术

Computational simulation of virtual patients reduces dataset bias and improves machine learning-based detection of ARDS from noisy heterogeneous ICU datasets

Published:2023 Issue: Volume: Page:1-11
ISSN:2644-1276
Container-title:IEEE Open Journal of Engineering in Medicine and Biology
language:
Short-container-title:IEEE Open J. Eng. Med. Biol.

Author:

Sharafutdinov Konstantin¹^ORCID,Fritsch Sebastian Johannes²,Iravani Mina¹,Ghalati Pejman Farhadi¹,Saffaran Sina³,Bates Declan G.³^ORCID,Hardman Jonathan G.⁴,Polzin Richard¹,Mayer Hannah²,Marx Gernot²,Bickenbach Johannes²,Schuppert Andreas¹

Affiliation:

1. Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany

2. SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany

3. School of Engineering, University of Warwick, Coventry, UK

4. School of Medicine, University of Nottingham, Nottingham, UK

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering

Link

http://xplorestaging.ieee.org/ielx7/8782705/8937520/10040737.pdf?arnumber=10040737

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2. A pretrain-finetune approach for improving model generalizability in outcome prediction of acute respiratory distress syndrome patients;International Journal of Medical Informatics;2024-06

3. Combined mechanistic modeling and machine-learning approaches in systems biology – A systematic literature review;Computer Methods and Programs in Biomedicine;2023-10

4. Developing an Artificial Intelligence-Based Representation of a Virtual Patient Model for Real-Time Diagnosis of Acute Respiratory Distress Syndrome;Diagnostics;2023-06-17