Abstract

Clinical Decision Support Systems (CDSSs) aim to help healthcare professionals in making more efficient decisions. Their task may be to help, e.g., with early diagnosis of a disease, creation of treatment plans, or following the effectiveness of a certain treatment. Recent advances in data-driven approaches and machine learning methods, combined with the increased availability of data, have pushed this field forward. However, the speed of uptake of such methods in routine clinical use is much slower than what we are used to e.g. in industrial or financial applications.

In this talk, we will go through several issues that we run into when developing machine learning approaches specifically for real-life healthcare settings. We will consider the need for explainability, dealing with incomplete data, and integration with other systems, as well as assessment of performance/cost-effectiveness/impact. We will do this at the hand of two developed decision support systems: one for assisting in diagnosis of dementias, and one for outcome prediction and treatment planning in traumatic brain injuries. What went well, what could be improved, and what can we learn from that?

Speaker: Mark van Gils

Affiliation: Research Professor, VTT Technical Research Center of Finland