Applications of Machine Learning on Natural Language Processing and Biomedical Data

Machine learning is ubiquitous in today’s society, with promising applications
in the field of natural language processing (NLP), so that computers can handle
human language better, and within the medical community, with the promise
of better treatments. Machine learning can be seen as a subfield of artificial
intelligence (AI), where AI is used to describe a machine that mimics cognitive
functions that humans associate with other human minds, such as learning or
problem solving.
In this thesis we explore how machine learning can be used to improve classification of picture, by using associated text. We then shift our focus to biomedical data, specifically heart transplantation patients. We show how the data can be represented as a graph database using the resource description framework (RDF).
After that we use the data with logistic regression and the Spark framework, to
perform feature search to predict the survival probability of the patients. In the
two last articles we use artificial neural networks (ANN) first to predict patient
survival, and compare it with a logistic regression approach, and last to predict the outcome of patients awaiting heart transplantation.
We plan to do simulation of different allocation policies, for donor hearts, using
these kind of ANNs, to be able to asses their impact on predicted earned survival
time.