Application of explainable ensemble artificial intelligence model to categorization of hemodialysis-patient and treatment using nationwide-real-world data in Japan.

Eiichiro KandaBogdan I EpureanuTaiji AdachiYuki TsurutaKan KikuchiNaoki KashiharaMasanori AbeIkuto MasakaneKosaku Nitta

Published in: PloS one (2020)

The clusters clearly categorized patient on their characteristics, and reflected their prognosis. Our real-world-data-based machine learning system is applicable to identifying high-risk hemodialysis patients in clinical settings, and has a strong potential to guide treatments and improve their prognosis.

Keyphrases

artificial intelligence
big data
machine learning
case report
deep learning
electronic health record
convolutional neural network
cross sectional
risk assessment
climate change
replacement therapy