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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…
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…
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…
MINTY: Rule-based Models that Minimize the Need for Imputing Features with Missing Values
Lena Stempfle, Fredrik D. Johansson
Rule models are often preferred in prediction tasks with tabular inputs as they can be easily interpreted using natural language and provide predictive performance on par with more…