We are pleased to be able to share a summary prepared by a student. A warm thank you to the anonymous student for letting us share this recap.
On May 8th, Christoph Lippert, a Professor in the area of digital health and machine learning at the Hasso Plattner Institute and at the University Potsdam held a lecture on “Machine Learning for Population-Based Health Studies”. The starting point of this lecture was to show a graphical representation of a typical patient flow through a health care system. This includes a patient’s contact with the health care system due to some symptom. Later on, some tests will be carried out by a physician and, after a lengthy period, the patient will receive an invasive treatment. It can also be the case that it will eventually be too late for any treatment. The current procedure was compared with what it is expected to be a patient flow in the future, where an individual undergoes continuous monitoring for diseases (e.g. through genetic risk assessment). Thus, it is expected that an individual will constantly receive early warnings, which lead him/her to involve a doctor at an early stage. Christoph highlighted how machine learning has the potential to help moving from the current patient flow to a better one, where people know early on their risks for developing certain conditions and can act accordingly. He highlighted how genetic differences across individuals can shed light on an individual’s risk factors for diseases, thus allowing for better and more individualised treatment options. Christoph proceeded by explaining basic concepts about genome-wide association studies and their goal of finding causal variants and/or markers that explain variance. He provided several examples, such as how phenotypes and population structures are correlated. The discussion was heated and insightful. Several attendees pointed out the need to consider the ethical implications of using genetics to predict health conditions. It was also mentioned how genome testing is cheap but, on a population level, this data does not provide meaningful information (i.e.. on a public health perspective). Furthermore, a question left hanging in the air concerned the degree of impact of lifestyle factors versus genes on an individual’s health status.