Machine Learning for Health

Reflection Questions

When preparing to present papers for the class, consider the following questions:

  • What problem in healthcare is this paper addressing?
  • What is the corresponding category of problem in ML that we can map this problem onto?
  • What machine learning approaches are the authors proposing/using?
  • Do standard ML methods do the trick?
  • What is special about the HC problem that the methods need to adapt to?
  • Were appropriate checks in place for model testing or avoiding model misspecification?
  • What type of confounders are present in the health care data? how does the model address those confounders?
  • How does the approach quantify uncertainty? Is uncertainty important here?
  • Are there limited numbers of samples? how are those addressed?
  • How can I determine whether a specific patient's sample is similar to others, or unique? (the n of 1 problem)
  • How is patient/patient group heterogeneity addressed?
  • How can you quantify the most important features of the data? Are the methods interpretable?
  • How are doctor/caregiver mistakes accounted for?
  • Are there possible biases introduced by the data or the ML method?
  • What are ways to combat those biases?
  • How would you explain to a doctor how the method worked, or why it arrived at a class label/decision?
  • what is the distance that needs to be covered before this method is deployed in a healthcare setting?