4 citations · 5 across the 8 of their papers we have counts for
9 papers
Protecting patient privacy in clinical foundation models: Technical and legal perspectives
Sana Tonekaboni, Lena Stempfle, Sasha Ronaghi +4
Clinical foundation models trained on large-scale patient data are increasingly used for decision support, screening, and public health planning. As deployment expands, privacy ris…
Clinically Grounded Privacy Evaluation of Medical LMs
Sasha Ronaghi, Sana Tonekaboni, Lena Stempfle +6
Medical language models (LMs) can memorize and reproduce protected health information, but privacy evaluations often focus on recovery of training text rather than disclosure under…
Evaluation without Generation: Non-Generative Assessment of Harmful Model Specialization with Applications to CSAM
Vinith M. Suriyakumar, Ayush Sekhari, Lena Stempfle +5
Auditing the fine-tunes of open-weight generative models for harmful specialization has become a new governance challenge for model hosting platforms. The standard toolkit, generat…
An Investigation of Memorization Risk in Healthcare Foundation Models
Sana Tonekaboni, Lena Stempfle, Adibvafa Fallahpour +2
Foundation models trained on large-scale de-identified electronic health records (EHRs) hold promise for clinical applications. However, their capacity to memorize patient informat…
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…
Sharing pattern submodels for prediction with missing values
Lena Stempfle, Ashkan Panahi, Fredrik D. Johansson
Missing values are unavoidable in many applications of machine learning and present challenges both during training and at test time. When variables are missing in recurring patter…