Advances in Biomedical Missing Data Imputation: A Survey

Advances in Biomedical Missing Data Imputation: A Survey

Ensuring data quality in biomedical sciences is crucial for reliable research outcomes, particularly as precision medicine continues to gain prominence. Missing values compromise data quality and can make it difficult to perform data-based studies. The origins of missing values in biomedical dataset...

Full description

Saved in:
Bibliographic Details
Main Authors: Miriam Barrabes, Maria Perera, Victor Novelle Moriano, Xavier Giro-I-Nieto, Daniel Mas Montserrat, Alexander G. Ioannidis
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Biomedical data imputation
data-centric AI
deep learning
genomics
health record
imputation
Online Access:https://ieeexplore.ieee.org/document/10795134/
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://ieeexplore.ieee.org/document/10795134/

Similar Items