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CS 5540 - Computational Techniques for Analyzing Clinical Data

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General Information Edit

CS5540 is a masters-level course that covers a wide range of clinical problems and their associated computational challenges. The practice of medicine is filled with digitally accessible information about patients, ranging from EKG readings to MRI images to electronic health records. This poses a huge opportunity for computer tools that make sense out of this data. Computation tools can be used to answer seemingly straightforward questions about a single patient's test results (“Does this patient have a normal heart rhythm?”), or to address vital questions about large populations (“Is there any clinical condition that affects the risks of Alzheimer”). In CS5540 we will look at many of the most important sources of clinical data and discuss the basic computational techniques used for their analysis, ranging in sophistication from current clinical practice to state-of-the-art research projects.

Prerequisites Edit

There are no pre-requisites beyond programming skill at the level of CS 2110, although some familiarity with elementary statistics and algorithms would be helpful.

Topics Covered Edit

  • Introduction to medical data and signals: examples drawn from hospital records, EEG, CT, MRI.
  • Methods for processing of 1D medical signals
  • Epilepsy detection from EEG signals
  • Statistically optimal estimation and detection of 1D signals
  • Methods for processing multi-dimensional medical signals
  • Statistically optimal estimation and detection
  • MRI image reconstruction
  • Image segmentation
  • Medical applications of machine learning
  • Classification, clustering and segmentation of tumor/non-tumor voxels from contrast-enhanced MRI of liver and brain
  • Classification of patients' disease state from imaging and non-imaging data

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