Machine learning methods for precision medicine: even small data can raise big challenges
Machine learning (ML) is the science of getting computers to act without being explicitly programmed.
ML typically follows a data-driven methodology where models are built from observed data
before making predictions on new data.
This talk will present several ML applications to precision medicine, an area of medicine
where decisions, treatment and follow-up are aimed to be tailored to each individual patient.
We present prototypical examples including breast cancer prognosis, early diagnosis
of undifferentiated arthritis or treatment response prediction of an immunotherapy against melanoma.
Such examples illustrate core ML concepts including multi-class prediction,
multitask or transfer learning, robustness, feature selection and stability.
At times where big data is ubiquitous, we discuss briefly in such a context why scarce data
can sometimes be even more challenging.