A novel missing data imputation approach based on clinical conditional Generative Adversarial Networks applied to EHR datasets-Reference-Cited by-同舟云学术

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A novel missing data imputation approach based on clinical conditional Generative Adversarial Networks applied to EHR datasets

Published:2023-09 Issue: Volume:163 Page:107188
ISSN:0010-4825
Container-title:Computers in Biology and Medicine
language:en
Short-container-title:Computers in Biology and Medicine

Author:

Bernardini Michele,Doinychko Anastasiia,Romeo Luca,Frontoni Emanuele,Amini Massih-Reza

Funder

Università Politecnica delle Marche

Università di Macerata

European Commission

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications

Reference38 articles.

1. An accurate and dynamic predictive model for a smart M-Health system using machine learning;Qureshi;Inform. Sci.,2020

2. TyG-er: An ensemble Regression Forest approach for identification of clinical factors related to insulin resistance condition using Electronic Health Records;Bernardini;Comput. Biol. Med.,2019

3. Computers and preventative diagnosis. A survey with bioinformatics examples of mitochondrial small open reading frame peptides as portents of a new generation of powerful biomarkers;Robson;Comput. Biol. Med.,2022

4. Gain: Missing data imputation using generative adversarial nets;Yoon,2018

5. NOREVA: Enhanced normalization and evaluation of time-course and multi-class metabolomic data;Yang;Nucleic Acids Res.,2020

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