66 citations · 74 across the 4 of their papers we have counts for
6 papers
Estimating Individualized Treatment Effects in Acute Ischemic Stroke with Causal Transformation Models (TRAM-DAG): A Multi-Centre Observational Study with External RCT Validation
Lisa Herzog, Oliver Dürr, Pascal Bühler +4
Personalized medicine in acute ischemic stroke requires moving beyond average treatment effects (ATE) to individualized treatment effect (ITE) estimates to support treatment decisi…
Outcome prediction and individualized treatment effect estimation in patients with large vessel occlusion stroke
Lisa Herzog, Pascal Bühler, Ezequiel de la Rosa +2
Mechanical thrombectomy has become the standard of care in patients with stroke due to large vessel occlusion (LVO). However, only 50% of successfully treated patients show a favor…
Explainability in mulimodal deep transformation models for stroke outcome prediction
Lisa Herzog, Jonas Brändli, Maurice Schneeberger +7
Multimodal prediction models based on imaging and clinical data are increasingly used for clinical decision support, yet their interpretability remains limited. We present multimod…
Deep transformation models for functional outcome prediction after acute ischemic stroke
Lisa Herzog, Lucas Kook, Andrea Götschi +6
In many medical applications, interpretable models with high prediction performance are sought. Often, those models are required to handle semi-structured data like tabular and ima…
Deep and interpretable regression models for ordinal outcomes
Lucas Kook, Lisa Herzog, Torsten Hothorn +2
Outcomes with a natural order commonly occur in prediction tasks and often the available input data are a mixture of complex data like images and tabular predictors. Deep Learning…
Integrating uncertainty in deep neural networks for MRI based stroke analysis
Lisa Herzog, Elvis Murina, Oliver Dürr +2
At present, the majority of the proposed Deep Learning (DL) methods provide point predictions without quantifying the models uncertainty. However, a quantification of the reliabili…