An NLP-Inspired Data Augmentation Method for Adverse Event Prediction Using an Imbalanced Healthcare Dataset

An NLP-Inspired Data Augmentation Method for Adverse Event Prediction Using an Imbalanced Healthcare Dataset

This paper proposes a data augmentation method for imbalanced healthcare datasets. This method was inspired by a data augmentation method in natural language processing (NLP) that generates synthetic sentences for training by replacing some words with similar words. The proposed method generates syn...

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Bibliographic Details
Main Authors: Tomoki Ishikawa, Takahiro Yakoh, Hisashi Urushihara
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Adverse event prediction
data augmentation
distributed representation
healthcare dataset
imbalanced dataset
Online Access:https://ieeexplore.ieee.org/document/9845410/

Internet

https://ieeexplore.ieee.org/document/9845410/

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