We are looking for Master Students to work on Cost-effective Clinical Pathways!

We are currently looking for Master students interested to work on projects at the intersection of machine learning and medicine.

Machine-Learning for the Identification of Cost-effective Clinical Pathways

Do you want to combine state-of-the-art machine learning (ML) algorithms with the pressing societal problem of high (and rising) healthcare costs? Are you motivated to work with interesting real-world data and excited to implement and apply machine learning algorithms?

High and rising healthcare costs are a significant challenge in many developed countries, which is expected to intensify with the demographic changes that will accelerate over the next decades. Hospitals constitute the largest provider category with over a third of all healthcare expenditures being spent on them. The shape of clinical pathways (what clinical services are provided to the patient and in what sequence) can significantly impact hospitals’ costs of providing care.

In this project, you will explore the potential of machine learning models to identify and develop (characteristics of) clinical pathways with low treatment costs and high quality of care. Depending on the background and experience of the student, we offer an applied as well as theoretical projects (and/or master thesis projects) focused on developing machine learning approaches to tackle the problem of:

• identifying cohorts of patients with similar characteristics and similar clinical pathways
• identifying key characteristics (and underlying treatment decisions) of low-cost clinical pathways
• developing new (out-of-sample) clinical pathways that lower costs at high quality medical service

We are looking for master students who have taken courses in machine learning, data science and/or statistics. Additionally, students should have a good command of Python programming language (and its scientific stack, such as Scipy, Numpy, Pandas etc.). Preferably, students have also worked with neural network/deep learning frameworks such as Pytorch.

To apply, please send your CV to this e-mail along with a short description of your motivation to work on this project.