3 papers
cs.LG2025
Prediction Models That Learn to Avoid Missing Values
Lena Stempfle, Anton Matsson, Newton Mwai +1
Handling missing values at test time is challenging for machine learning models, especially when aiming for both high accuracy and interpretability. Established approaches often ad…
cs.LG2025
Handling missing values in clinical machine learning: Insights from an expert study
Lena Stempfle, Arthur James, Julie Josse +2
Inherently interpretable machine learning (IML) models offer valuable support for clinical decision-making but face challenges when features contain missing values. Traditional app…
cs.LG2024
How Should We Represent History in Interpretable Models of Clinical Policies?
Anton Matsson, Lena Stempfle, Yaochen Rao +3
Modeling policies for sequential clinical decision-making based on observational data is useful for describing treatment practices, standardizing frequent patterns in treatment, an…