Machine Learning in Medicine Seminar

  • Remote Access - Zoom
  • Virtual Presentation - ET
  • KARSTEN BORGWARDT
  • Full Professor of Data Mining
  • Deputy Head, Department of Biosystems Science and Engineering
  • ETH Z├╝rich
Seminars

Machine Learning in Medicine: Early Recognition of Sepsis

Sepsis is a major cause of mortality in intensive care units around the world. If recognized early, it can often be treated successfully, but early prediction of sepsis is an extremely difficult task in clinical practice. The data wealth from intensive care units that is increasingly becoming available for research now allows to study this problem of predicting sepsis using machine learning and data mining approaches. In this talk, I will describe our efforts towards data-driven early recognition of sepsis.

About the Speaker  |  ETH Machine Learning & Computational Biology Lab

About MLxMed SeminarMedicine is complex and data-driven while discovery and decision making are increasingly enabled by machine learning. Machine learning has the potential to support, enable and improve medical discovery and clinical decision making in areas such as medical imaging, cancer diagnostics, precision medicine, clinical trials, and electronic health records. This seminar series focuses on new algorithms, real-world deployment, and future trends in machine learning in medicine.

Hosted by the Department of Biomedical Informatics, joint with the University of Pittsburgh, UPMC, and Carnegie Mellon University.

Zoom Participation. See announcement.

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