Extremely missing numerical data in Electronic Health Records for machine learning can be managed through simple imputation methods considering informative missingness: A comparative of solutions in a COVID-19 mortality case study-Reference-Cited by-同舟云学术

Extremely missing numerical data in Electronic Health Records for machine learning can be managed through simple imputation methods considering informative missingness: A comparative of solutions in a COVID-19 mortality case study

Published:2023-12 Issue: Volume:242 Page:107803
ISSN:0169-2607
Container-title:Computer Methods and Programs in Biomedicine
language:en
Short-container-title:Computer Methods and Programs in Biomedicine

Author:

Ferri Pablo^ORCID,Romero-Garcia Nekane,Badenes Rafael^ORCID,Lora-Pablos David^ORCID,Morales Teresa García,Gómez de la Cámara Agustín,García-Gómez Juan M.,Sáez Carlos^ORCID

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications,Software

Reference55 articles.

1. Machine learning approaches for electronic health records phenotyping: a methodical review;Yang;J. Am. Med. Inform. Assoc.,2022

2. Practical strategies for extreme missing data imputation in dementia diagnosis;McCombe;IEEE J. Biomed. Health Inform.,2022

3. Development of a ‘meta-model’ to address missing data, predict patient-specific cancer survival and provide a foundation for clinical decision support;Baron;J. Am. Med. Inform. Assoc.,2021

4. Missing data in clinical studies: issues and methods;Ibrahim;JCO,2012

5. Strategies for handling missing data in electronic health record derived data;Wells;EGEMS (Wash DC),2013

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