1.6k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of BremenDE11 papers
- Ludwig-Maximilians-Universität MünchenDE7 papers
- University of CopenhagenDK7 papers
- LMU KlinikumDE3 papers
- Munich Center for Machine Learning3 papers
- University of OsloNO3 papers
- University of ViennaAT3 papers
- Institut für Medizinische Informationsverarbeitung, Biometrie und EpidemiologieDE2 papers
- University Medical Center UtrechtNL2 papers
- University of WarsawPL2 papers
- Warsaw University of TechnologyPL2 papers
- Amsterdam University Medical CentersNL1 paper
18 papers
Machine Learning in Epidemiology
Marvin N. Wright, Lukas Burk, Pegah Golchian +3
In the age of digital epidemiology, epidemiologists are faced by an increasing amount of data of growing complexity and dimensionality. Machine learning is a set of powerful tools…
Reduction Techniques for Survival Analysis
Johannes Piller, Léa Orsini, Simon Wiegrebe +6
In this work, we discuss what we refer to as reduction techniques for survival analysis, that is, techniques that "reduce" a survival task to a more common regression or classifica…
Robot Pouring: Identifying Causes of Spillage and Selecting Alternative Action Parameters Using Probabilistic Actual Causation
Jaime Maldonado, Jonas Krumme, Christoph Zetzsche +2
In everyday life, we perform tasks (e.g., cooking or cleaning) that involve a large variety of objects and goals. When confronted with an unexpected or unwanted outcome, we take co…
A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data
Lukas Burk, John Zobolas, Bernd Bischl +3
This work presents the first large-scale neutral benchmark experiment focused on single-event, right-censored, low-dimensional survival data. Benchmark experiments are essential in…
A Guide to Feature Importance Methods for Scientific Inference
Fiona Katharina Ewald, Ludwig Bothmann, Marvin N. Wright +3
While machine learning (ML) models are increasingly used due to their high predictive power, their use in understanding the data-generating process (DGP) is limited. Understanding…
Interpretable Machine Learning for Survival Analysis
Sophie Hanna Langbein, Mateusz Krzyziński, Mikołaj Spytek +3
With the spread and rapid advancement of black box machine learning models, the field of interpretable machine learning (IML) or explainable artificial intelligence (XAI) has becom…
Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review
Jan-Philipp Redlich, Friedrich Feuerhake, Joachim Weis +9
In recent years, the diagnosis of gliomas has become increasingly complex. Analysis of glioma histopathology images using artificial intelligence (AI) offers new opportunities to s…
Risk-based decision making: estimands for sequential prediction under interventions
Kim Luijken, Paweł Morzywołek, Wouter van Amsterdam +14
Prediction models are used amongst others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an interve…
survex: an R package for explaining machine learning survival models
Mikołaj Spytek, Mateusz Krzyziński, Sophie Hanna Langbein +3
Due to their flexibility and superior performance, machine learning models frequently complement and outperform traditional statistical survival models. However, their widespread a…
Interpreting Deep Neural Networks with the Package innsight
Niklas Koenen, Marvin N. Wright
The R package innsight offers a general toolbox for revealing variable-wise interpretations of deep neural networks' predictions with so-called feature attribution methods. Aside f…
Conditional Feature Importance for Mixed Data
Kristin Blesch, David S. Watson, Marvin N. Wright
Despite the popularity of feature importance (FI) measures in interpretable machine learning, the statistical adequacy of these methods is rarely discussed. From a statistical pers…
Unifying local and global model explanations by functional decomposition of low dimensional structures
Munir Hiabu, Joseph T. Meyer, Marvin N. Wright
We consider a global representation of a regression or classification function by decomposing it into the sum of main and interaction components of arbitrary order. We propose a ne…
Nachhaltige Strategien gegen die COVID-19-Pandemie in Deutschland im Winter 2021/2022
Viola Priesemann, Eberhard Bodenschatz, Sandra Ciesek +18
In this position paper, a large group of interdisciplinary experts outlines response strategies against the spread of SARS-CoV-2 in the winter of 2021/2022 in Germany. We review th…
Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process
Christoph Molnar, Timo Freiesleben, Gunnar König +3
Scientists and practitioners increasingly rely on machine learning to model data and draw conclusions. Compared to statistical modeling approaches, machine learning makes fewer exp…
Time-dependent mediators in survival analysis: Modelling direct and indirect effects with the additive hazards model
Odd O. Aalen, Mats J. Stensrud, Vanessa Didelez +3
We discuss causal mediation analyses for survival data and propose a new approach based on the additive hazards model. The emphasis is on a dynamic point of view, that is, understa…
Model-agnostic Feature Importance and Effects with Dependent Features -- A Conditional Subgroup Approach
Christoph Molnar, Gunnar König, Bernd Bischl +1
The interpretation of feature importance in machine learning models is challenging when features are dependent. Permutation feature importance (PFI) ignores such dependencies, whic…
Insights into the "cross-world" independence assumption of causal mediation analysis
Ryan M. Andrews, Vanessa Didelez
Causal mediation analysis is a useful tool for epidemiological research, but it has been criticized for relying on a "cross-world" independence assumption that is empirically diffi…
Hyperparameters and Tuning Strategies for Random Forest
Philipp Probst, Marvin Wright, Anne-Laure Boulesteix
The random forest algorithm (RF) has several hyperparameters that have to be set by the user, e.g., the number of observations drawn randomly for each tree and whether they are dra…