Machine Learning for Health: Personalized Models for Forecasting of Alzheimer Disease Progression

TL;DR

This thesis develops personalized machine learning models, including Gaussian processes and Cox models, to forecast Alzheimer's disease progression and clinical status changes using large clinical trial datasets, aiding clinical trial design.

Contribution

Introduces personalized Gaussian process and Cox models for predicting Alzheimer's progression and conversion, enhancing forecasting accuracy over existing methods.

Findings

Models accurately predict cognitive decline over 6-24 months.

Effective in forecasting conversion to Alzheimer's within 2 years.

Collaborative approach improves clinical trial planning.

Abstract

In this thesis the aim is to work on optimizing the modern machine learning models for personalized forecasting of Alzheimer Disease (AD) Progression from clinical trial data. The data comes from the TADPOLE challenge, which is one of the largest publicly available datasets for AD research (ADNI dataset). The goal of the project is to develop machine learning models that can be used to perform personalized forecasts of the participants cognitive changes (e.g., ADAS-Cog13 scores) over the time period of 6,12, 18 and 24 months in the future and the change in Clinical Status (CS) i.e., whether a person will convert to AD within 2 years or not. This is important for informing current clinical trials and better design of future clinical trials for AD. We will work with personalized Gaussian processes as machine learning models to predict ADAS-Cog13 score and Cox model along with a classifier to predict the conversion in a patient within 2 years.This project is done with the collaboration with researchers from the MIT MediaLab.