Interpretability

Extracting New Temporal Features to Improve the Interpretability of Undiagnosed Type 2 Diabetes Mellitus Prediction Models

Type 2 diabetes mellitus (T2DM) often results in high morbidity and mortality. In addition, T2DM presents a substantial financial burden for individuals and their families, health systems, and societies. According to studies and reports, globally, …

FWO-ARRS project

Improving interpretability and performance of risk prediction models for decision support in clinical environments Main host institution: KU Leuven Supervisor: Katrien Verbert (Informatics Section, KU Leuven) Foreign host institution: University of Maribor

Interpretability of machine learning based prediction models in healthcare

There is a need of ensuring machine learning models that are interpretable. Higher interpretability of the model means easier comprehension and explanation of future predictions for end-users. Further, interpretable machine learning models allow …