Domain Adaptation through Deep Neural Networks for Health Informatics

Donati, Lorenzo (2017) Domain Adaptation through Deep Neural Networks for Health Informatics. [Laurea magistrale], Università di Bologna, Corso di Studio in Ingegneria informatica [LM-DM270]
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Abstract

The PreventIT project is an EU Horizon 2020 project aimed at preventing early functional decline at younger old age. The analysis of causal links between risk factors and functional decline has been made possible by the cooperation of several research institutes' studies. However, since each research institute collects and delivers different kinds of data in different formats, so far the analysis has been assisted by expert geriatricians whose role is to detect the best candidates among hundreds of fields and offer a semantic interpretation of the values. This manual data harmonization approach is very common in both scientific and industrial environments. In this thesis project an alternative method for parsing heterogeneous data is proposed. Since all the datasets represent semantically related data, being all made from longitudinal studies on aging-related metrics, it is possible to train an artificial neural network to perform an automatic domain adaptation. To achieve this goal, a Stacked Denoising Autoencoder has been implemented and trained to extract a domain-invariant representation of the data. Then, from this high-level representation, multiple classifiers have been trained to validate the model and ultimately to predict the probability of functional decline of the patient. This innovative approach to the domain adaptation process can provide an easy and fast solution to many research fields that now rely on human interaction to analyze the semantic data model and perform cross-dataset analysis. Functional decline classifiers show a great improvement in their performance when trained on the domain-invariant features extracted by the Stacked Denoising Autoencoder. Furthermore, this project applies multiple deep neural network classifiers on top of the Stacked Denoising Autoencoder representation, achieving excellent results for the prediction of functional decline in a real case study that involves two different datasets.

Abstract
Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Donati, Lorenzo
Relatore della tesi
Correlatore della tesi
Scuola
Corso di studio
Ordinamento Cds
DM270
Parole chiave
Neural Network,Deep Learning,Unsupervised Learning,Deep Neural Network,Autoencoder,Stacked Denoising Autoencoder,Domain Adaptation,Long Short-Term Memory,Big Data,Health Informatics
Data di discussione della Tesi
20 Dicembre 2017
URI

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