| Predicting progression and cognitive decline in amyloid-positive patients with Alzheimer’s disease HV Dansson, L Stempfle, H Egilsdóttir, A Schliep, E Portelius, K Blennow, ... Alzheimer's Research & Therapy 13, 1-16, 2021 | 27 | 2021 |
| Sharing pattern submodels for prediction with missing values L Stempfle, A Panahi, FD Johansson Proceedings of the AAAI Conference on Artificial Intelligence 37 (8), 9882-9890, 2023 | 8 | 2023 |
| Minty: Rule-based models that minimize the need for imputing features with missing values L Stempfle, F Johansson International Conference on Artificial Intelligence and Statistics, 964-972, 2024 | 1 | 2024 |
| Applying machine learning to high-dimensional proteomics datasets for the identification of Alzheimer’s disease biomarkers C Ivarsson Orrelid, O Rosberg, S Weiner, FD Johansson, J Gobom, ... Fluids and Barriers of the CNS 22 (1), 23, 2025 | | 2025 |
| How Should We Represent History in Interpretable Models of Clinical Policies? A Matsson, L Stempfle, Y Rao, ZR Margolin, HJ Litman, FD Johansson arXiv preprint arXiv:2412.07895, 2024 | | 2024 |
| Expert Study on Interpretable Machine Learning Models with Missing Data L Stempfle, A James, J Josse, T Gauss, FD Johansson arXiv preprint arXiv:2411.09591, 2024 | | 2024 |
| Interpretable machine learning models for predicting with missing values L Stempfle PQDT-Global, 2023 | | 2023 |
| Learning replacement variables in interpretable rule-based models L Stempfle, FD Johansson ICML 3rd Workshop on Interpretable Machine Learning in Healthcare (IMLH), 0 | | |
Fredrik D. JohanssonAssociate Professor, Chalmers University of TechnologyVerified email at chalmers.se