Conditional autoencoder pre-training and optimization algorithms for personalized care of hemophiliac patients

This paper presents the use of deep conditional autoencoder to predict the effect of treatments for patients suffering from hemophiliac disorders. Conditional autoencoder is a semi-supervised model that learns an abstract representation of the data and provides conditional reconstruction capabilitie...

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Published in:Frontiers in artificial intelligence Vol. 6; p. 1048010
Main Authors: Buche, Cédric, Lasson, François, Kerdelo, Sébastien
Format: Journal Article
Language:English
Published: Switzerland Frontiers Media S.A 25.01.2023
Subjects:
Artificial Intelligence
autoencoder
Computer Science
conditional pre-training
deep learning
global assays
hemophilia
incremental optimization
deep learning
hemophilia
conditional pre-training
autoencoder
global assays
incremental optimization
ISSN:2624-8212, 2624-8212
Online Access:Get full text
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Summary:This paper presents the use of deep conditional autoencoder to predict the effect of treatments for patients suffering from hemophiliac disorders. Conditional autoencoder is a semi-supervised model that learns an abstract representation of the data and provides conditional reconstruction capabilities. Such models are suited to problems with limited and/or partially observable data, common situation for data in medicine. Deep conditional autoencoders allow the representation of highly non-linear functions which makes them promising candidates. However, the optimization of parameters and hyperparameters is particularly complex. For parameter optimization, the classical approach of random initialization of weight matrices works well in the case of simple architectures, but is not feasible for deep architectures. For hyperparameter optimization of deep architectures, the classical cross-validation method is costly. In this article, we propose solutions using a conditional pre-training algorithm and incremental optimization strategies. Such solutions reduce the variance of the estimation process and enhances convergence of the learning algorithm. Our proposal is applied for personalized care of hemophiliac patients. Results show better performances than generative adversarial networks (baseline) and highlight the benefits of your contribution to predict the effect of treatments for patients.
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Reviewed by: Guilherme De Alencar Barreto, Federal University of Ceara, Brazil; Paul Honeine, EA4108 Laboratoire d'informatique, de Traitement de l'Information et des Systèmes (LITIS), France
Edited by: Sohan Seth, University of Edinburgh, United Kingdom
This article was submitted to Medicine and Public Health, a section of the journal Frontiers in Artificial Intelligence
ISSN:2624-8212
2624-8212
DOI:10.3389/frai.2023.1048010