Machine learning in medicine

Image: Jesse Orrico

Applications of modern machine learning methods to problems in medicine and healthcare.

The Data Science and its Applications research group plays a significant role in several large health research collaborations, including the curATime (Cluster for Atherothrombosis and Individualized Medicine) consortium and the MIRACLE (A Machine learning approach to Identify patients with Resected non-small-cell lung cAnCer with high risk of reLapsE) project, focussing on methods in genomics and survival analysis, respectively.

Possible topics

  • Hypothesis discovery on biomedical knowledge graphs
  • Applications of graph neural networks to multi-modal medical data
  • Cross-species transfer learning
  • Information retrieval using open medical databases
  • Biologically-informed deep learning
  • Generative models for electronic health records
  • High-dimensional feature extraction for multi-omic time-to-event models

Further reading

  • Sören Richard Stahlschmidt, Benjamin Ulfenborg, Jane Synnergren, Multimodal deep learning for biomedical data fusion: a review, Briefings in Bioinformatics, Volume 23, Issue 2, March 2022, bbab569,
  • Elmarakeby, H.A., Hwang, J., Arafeh, R. et al. Biologically informed deep neural network for prostate cancer discovery. Nature 598, 348–352 (2021).
Sebastian Vollmer
Sebastian Vollmer
Professor for Applications of Machine Learning

My research interests lie at the interface of applied probability, statistical inference and machine learning.

David Antony Selby
David Antony Selby
Senior Researcher

My research interests include latent variable modelling, reproducibility, citation networks and applications of statistics and machine learning to healthcare.