2 citations · 2 across the 1 of their papers we have counts for
4 papers
Improving post-operative discharge destination prediction of geriatric patients with generative data augmentation
Pegah Golchian, Pauline Maier, Thomas Kocar +1
Data scarcity challenges the development and implementation of innovative healthcare solutions. In geriatrics, fall-related injuries are a major cause of hospitalization, functiona…
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…
Imputation Uncertainty in Interpretable Machine Learning Methods
Pegah Golchian, Marvin N. Wright
In real data, missing values occur frequently, which affects the interpretation with interpretable machine learning (IML) methods. Recent work considers bias and shows that model e…
Conditional Feature Importance with Generative Modeling Using Adversarial Random Forests
Kristin Blesch, Niklas Koenen, Jan Kapar +4
This paper proposes a method for measuring conditional feature importance via generative modeling. In explainable artificial intelligence (XAI), conditional feature importance asse…