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 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.