论文标题
健康的机器学习：预测阿尔茨海默氏病进展的个性化模型
Machine Learning for Health: Personalized Models for Forecasting of Alzheimer Disease Progression
论文作者
论文摘要
在本论文中，目的是优化现代机器学习模型，以通过临床试验数据对阿尔茨海默氏病（AD）进行个性化预测。数据来自Tadpole挑战，该挑战是广告研究（ADNI数据集）的最大公开数据集之一。该项目的目的是开发机器学习模型，该模型可用于对参与者的认知变化（例如ADAS-COG13分数）进行个性化预测，以将来6,12、18和24个月以及临床状况变化（CS）的变化（即是否在2年内转换为广告）。这对于为当前的临床试验和AD的未来临床试验的设计提供了更好的设计很重要。我们将与个性化的高斯流程一起作为机器学习模型，以预测ADAS-COG13分数和COX模型以及分类器，以预测2年内患者的转化。该项目与MIT Medialab的研究人员合作完成。
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.
