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Machine Learning for Personalized Medicine
Do you want to combine statistics, machine learning (ML), and artificial intelligence (AI) algorithms with important medical applications? Are you motivated to work with interesting real-world data and excited to implement and apply machine learning algorithms to produce personalized decision support tools?
ML-driven precision medicine offers groundbreaking potential to revolutionize healthcare and medical applications by using ML and AI models to analyze complex patient characteristics and treatment outcomes in unprecedented detail. However, implementing AI methods in healthcare faces significant challenges, especially in dealing with the complexities of real-world healthcare data. Despite some successful implementations, there is a clear need for a more comprehensive, interdisciplinary, and patient-centric approach to integrating AI and ML infrastructure into the healthcare system. Currently, we are at a pivotal moment where all the necessary components – complex data, AI capabilities, and clinical demand – are in place, and practical implementation is the next needed step.
ML-driven precision medicine offers groundbreaking potential to revolutionize healthcare and medical applications by using ML and AI models to analyze complex patient characteristics and treatment outcomes in unprecedented detail. However, implementing AI methods in healthcare faces significant challenges, especially in dealing with the complexities of real-world healthcare data. Despite some successful implementations, there is a clear need for a more comprehensive, interdisciplinary, and patient-centric approach to integrating AI and ML infrastructure into the healthcare system. Currently, we are at a pivotal moment where all the necessary components – complex data, AI capabilities, and clinical demand – are in place, and practical implementation is the next needed step.
If you want to be part of this transformative process and create prototypical decision support tools, do not hesitate to apply for a master thesis with us! In this project, you will explore the potential of machine learning models to develop personalized and explainable treatment suggestions tailored to the complex biomedical characteristics of individual patients. Depending on the background and experience of the student, we offer more applied as well as more theoretical master thesis projects focusing on machine learning tools including
personalized and explainable treatment outcome prediction for patients,
developing optimal and personalized treatment suggestions with machine learning,
creating and implementing personalized decision support tools,
estimation of heterogenous counterfactual treatment outcomes.
The medical applications include oncology, organ transplantation, rheumatic diseases, and ICU.
If you want to be part of this transformative process and create prototypical decision support tools, do not hesitate to apply for a master thesis with us! In this project, you will explore the potential of machine learning models to develop personalized and explainable treatment suggestions tailored to the complex biomedical characteristics of individual patients. Depending on the background and experience of the student, we offer more applied as well as more theoretical master thesis projects focusing on machine learning tools including
personalized and explainable treatment outcome prediction for patients, developing optimal and personalized treatment suggestions with machine learning, creating and implementing personalized decision support tools, estimation of heterogenous counterfactual treatment outcomes.
The medical applications include oncology, organ transplantation, rheumatic diseases, and ICU.
You should be enrolled in a Master program at UZH or ETH with a solid quantitive aspect. You should have strong programming skills (preferred Python including Pytorch) and experience with machine learning. Biological and medical knowledge is a strong plus. However, the most important requirements are a lot of motivation and curiosity!
You can apply for this Master thesis by sending a CV together with a short description of your motivation to join our lab to Dr. Manuel Schürch (manuel.schuerch@uzh.ch).
You should be enrolled in a Master program at UZH or ETH with a solid quantitive aspect. You should have strong programming skills (preferred Python including Pytorch) and experience with machine learning. Biological and medical knowledge is a strong plus. However, the most important requirements are a lot of motivation and curiosity!
You can apply for this Master thesis by sending a CV together with a short description of your motivation to join our lab to Dr. Manuel Schürch (manuel.schuerch@uzh.ch).