Automatic Feature Interaction Learning for Alzheimer's Disease Diagnosis using Factorization Models

Automatic Feature Interaction Learning for Alzheimer’s Disease Diagnosis using Factorization Models

Title: Automatic Feature Interaction Learning for Alzheimer’s Disease Diagnosis using Factorization Models

 

Type: Master Thesis

Student: Raphael Ronge

Supervisor: Sebastian Pölsterl, Christian Wachinger

Status: Running

 

Abstract:

It is possible to divide the slowly progressing neurodegenerative Alzheimer’s disease into three groups: normal cognition (healthy patients), mild cognitive impairment (patients with first sings of cognitive malfunction) and Alzheimer’s disease patients. The current state of the art medical research uses different biomarker combinations to classify Alzheimer’s, but does not pay attention to interactions of biomarkers. In my Master Thesis, I try to find and model these interactions explicitly to classify patients of the three groups and gain a better understanding of biomarker interactions. I am going to leverage the research that is already available in the field of advertisement prediction. To predict personalized advertisements, online platforms need to use user data that is mostly categorical (e.g. gender, location, last visited item on website, …) and highly sparse. Therefore, they rely on feature interactions, otherwise a prediction would be impossible. In my Master Thesis, I will try different factorization models to find the best one to classify patients from the Alzheimer’s Disease Neuroimaging Initiative reliably. In addition, I will model biomarker interactions and try to find the most important ones to classify patients reliably.