Adaptation of Autoencoder for Sparsity Reduction From Clinical Notes
Representation Learning
- URL: http://arxiv.org/abs/2209.12831v1
- Date: Mon, 26 Sep 2022 16:37:37 GMT
- Title: Adaptation of Autoencoder for Sparsity Reduction From Clinical Notes
Representation Learning
- Authors: Thanh-Dung Le, Rita Noumeir, Jerome Rambaud, Guillaume Sans, and
Philippe Jouvet
- Abstract summary: We propose an autoencoder learning algorithm to take advantage of sparsity reduction in clinical note representation.
The motivation was to determine how to compress sparse, high-dimensional data by reducing the dimension of the clinical note representation feature space.
The proposed approach provided overall performance gains of up to 3% for each evaluation.
- Score: 0.19573380763700707
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: When dealing with clinical text classification on a small dataset recent
studies have confirmed that a well-tuned multilayer perceptron outperforms
other generative classifiers, including deep learning ones. To increase the
performance of the neural network classifier, feature selection for the
learning representation can effectively be used. However, most feature
selection methods only estimate the degree of linear dependency between
variables and select the best features based on univariate statistical tests.
Furthermore, the sparsity of the feature space involved in the learning
representation is ignored. Goal: Our aim is therefore to access an alternative
approach to tackle the sparsity by compressing the clinical representation
feature space, where limited French clinical notes can also be dealt with
effectively. Methods: This study proposed an autoencoder learning algorithm to
take advantage of sparsity reduction in clinical note representation. The
motivation was to determine how to compress sparse, high-dimensional data by
reducing the dimension of the clinical note representation feature space. The
classification performance of the classifiers was then evaluated in the trained
and compressed feature space. Results: The proposed approach provided overall
performance gains of up to 3% for each evaluation. Finally, the classifier
achieved a 92% accuracy, 91% recall, 91% precision, and 91% f1-score in
detecting the patient's condition. Furthermore, the compression working
mechanism and the autoencoder prediction process were demonstrated by applying
the theoretic information bottleneck framework.
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