Current representations of features in clinical data may underrepresent differences, in the case of features reduced to binary values, or over-represent them, in the case of features which are kept at their exact value. I aim to critically and quantitatively evaluate different feature representations. In addition to the representations that are already widely used, I introduce two new representations and compare all of these with the aim of defining a methodology for choosing the best set of features, representations, and model for building predictive models in a clinical setting. I evaluate the models based on their accuracy in predicting the outcomes of ICU patients from the MIMIC II & III databases.

I’ve been interested in machine learning ever since I took 6.034, and I think it can be very impactful in changing the way that healthcare is administered. I’ve spent the last two summers doing data science internships at health-focused companies, and I am excited to delve deeper into a large medical database and use a variety of machine learning techniques in this project.