Alexander Gerharz
Technical University of Dortmund
7 Papers
11 Citations
Alexander Gerharz is an academic researcher from Technical University of Dortmund. The author has contributed to research in topics: Computer science & Medicine. The author has co-authored 2 publications.
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Papers
Disease-dependent variations in the timing and causes of readmissions in Germany: A claims data analysis for six different conditions
Carmen Ruff,Alexander Gerharz,Andreas Groll,Felicitas Stoll,Lucas Wirbka,Walter E. Haefeli,Andreas D. Meid +6 more
TL;DR: In this article, the authors used German health insurance claims (AOK, 2011-2016) of patients ≥ 65 years hospitalized for acute myocardial infarction (AMI), heart failure (HF), a composite of stroke, transient ischemic attack, or atrial fibrillation (S/AF), chronic obstructive pulmonary disease (COPD), type 2 diabetes mellitus, or osteoporosis to identify hospital readmissions within 30 or 90 days.
Predicting Hospital Readmissions from Health Insurance Claims Data: A Modeling Study Targeting Potentially Inappropriate Prescribing.
Alexander Gerharz,Carmen Ruff,Lucas Wirbka,Felicitas Stoll,Walter E. Haefeli,Andreas Groll,Andreas D. Meid +6 more
TL;DR: PIP successfully predicted readmissions for most diseases, opening the possibility for interventions to improve these modifiable risk factors.
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Journal Article
Machine Learning for Multi-Output Regression: When should a holistic multivariate approach be preferred over separate univariate ones?
TL;DR: This work compares tree-based ensembles used for predicting univariate responses in extensive simulations to help in answering the primary question when to use multivariate ensemble techniques.
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Contextual Shift Method (CSM)
Gernot Schmitz,Daniel Wilmes,Alexander Gerharz,Daniel Shoreview Horn,Emmanuel Müller +4 more
TL;DR: This study addresses the limitations of Explainable AI methods by introducing the Contextual Shift Method (CSM), which generates meaningful, realistic artificial data points, reducing the creation of points in low data density areas and improving model interpretability.
•Posted Content
Deducing neighborhoods of classes from a fitted model.
TL;DR: A new kind of interpretable machine learning method is presented, which can help to understand the partitioning of the feature space into predicted classes in a classification model using quantile shifts.