Exploiting mutual information for the imputation of static and dynamic mixed-type clinical data with an adaptive k-nearest neighbours approach

Exploiting mutual information for the imputation of static and dynamic mixed-type clinical data with an adaptive k-nearest neighbours approach

Abstract Background Clinical registers constitute an invaluable resource in the medical data-driven decision making context. Accurate machine learning and data mining approaches on these data can lead to faster diagnosis, definition of tailored interventions, and improved outcome prediction. A typic...

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Bibliographic Details
Main Authors: Erica Tavazzi, Sebastian Daberdaku, Rosario Vasta, Andrea Calvo, Adriano Chiò, Barbara Di Camillo
Format: Article
Language:English
Published: BMC 2020-08-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Imputation
Missing data
K-nearest neighbours
Mutual information
Naïve Bayes
Clinical datasets
Online Access:http://link.springer.com/article/10.1186/s12911-020-01166-2

Internet

http://link.springer.com/article/10.1186/s12911-020-01166-2

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