Anne-Christin Hauschild

Evaluating EMOO as a Metric-Balanced Ensemble Framework for Multi-Objective Optimization in Imbalanced Medical Data

Maryam Moradpour, Zully Ritter, Anne-Christin Hauschild

INTRODUCTION: Class imbalance can hinder reliable detection of clinically relevant outcomes in binary clinical prediction. In this study, the positive class was predefined as the clinically relevant minority outcome; therefore, accuracy-only hyperparameter selection can favor the majority class and reduce sensitivity for …

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FedDRAW: Federated Dual Reputation Annealing Weighting for Heterogeneous Multi-Institutional Chest Radiograph Classification

Maryam Moradpour, Anne-Christin Hauschild

Artificial intelligence models are promising for medical diagnosis, but they require large numbers of unbiased data, which in medicine are distributed across hospitals and cannot be centralized to protect patient privacy. Federated Learning (FL) addresses this, since hospitals train one shared diagnostic …

Evaluating Graph Neural Network Architectures for Multi-Omics Cancer Subtyping using Methylation and Gene Expression Profiles

Julia Schirmacher, M. Mäurer, Jacqueline Michelle Metsch, Hryhorii Chereda et autres

Abstract Motivation Graph Neural Networks (GNNs) have gained increasing interest in the biomedical domain, as the integration of prior knowledge and deep neural networks has the potential to enhance insights into molecular processes and disease mechanisms. However, a comprehensive and systematic assessment …

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Feasibility of chronic kidney disease prediction using bilateral kidney ultrasound and foundation model representations

Nghiem Vo, Anh-Tien Nguyen, Thong N. H. Vo, Ga-Yeon Yang et autres

kr, de, us, sk (code pays fourni par la source)

Federated Dynamic Weighting for Heterogeneous Healthcare Data

Maryam Moradpour, Anne-Christin Hauschild

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Effects of Non-IID Distributions in Lung Cancer Data on Survival Prediction with Federated Ensemble Learning

Linus Weber, Anne-Christin Hauschild, Michael Altenbuchinger, Ulrich Sax et autres

A common challenge in Federated Learning (FL) is that distribution shifts between clients, or Non-IIDness, decrease global model performance. Non-IIDness means that data is not independently and identically distributed between participating sites. Stronger distributional shifts lead to greater reductions of model performance. …

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Evaluating the Potential of Machine Learning for Discharge Management on Routine Health Insurance Data

Zully Ritter, M. Mäurer, Jacqueline Michelle Metsch, Lisa Weller et autres

Machine learning (ML) has great potential in healthcare, especially with large structured data. Routine health insurance claims (HIC) data are a valuable resource, comprising standardized longitudinal patient information. However, to fully leverage ML in HIC, challenges such as large data volumes, variability, …

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FederatedRSF : Federated Random Survival Forests for Partially Overlapping Medical Data

Maryam Moradpour, Jonas Harriehausen, Amirreza Aleyasin, Lion Philipp Wolf et autres

Multi-center survival prediction can improve robustness and generalizability, yet privacy regulations and institutional governance often prevent pooling patient-level clinical and genomic data across institutions. In practice, deployment is further complicated by feature-space heterogeneity, in which sites collect different covariates or use different …

FederatedRSF : Federated Random Survival Forests for Partially Overlapping Medical Data

Maryam Moradpour, Jonas Harriehausen, Amirreza Aleyasin, Lion Philipp Wolf et autres

Multi-center survival prediction can improve robustness and generalizability, yet privacy regulations and institutional governance often prevent pooling patient-level clinical and genomic data across institutions. In practice, deployment is further complicated by feature-space heterogeneity, in which sites collect different covariates or use different …

us, de (code pays fourni par la source)

A Federated Artificial Intelligence Framework for Optimizing Pancreatic Cancer Treatment – a Technical Case Report

Anne-Christin Hauschild, Amirreza Aleyasin, Nils Beyer, Lisa Fricke et autres

Introduction: Ideally, centrally collecting and analyzing patient data with appropriate consent would provide optimal data quality and predictive performance; however, this approach is often not feasible in practice. Federated Learning (FL) or Federated Artificial Intelligence architectures have shown [for full text, please …

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Potential of Machine Learning for Discharge Management Using Routine Health Insurance Data

Zully Ritter, M. Mäurer, Jacqueline Michelle Beinecke, Lisa Weller et autres

Introduction: The use of artificial intelligence, particularly machine learning models, to develop predictive models from health insurance routine data is becoming increasingly common [ref:1], [ref:2]. However, it’s not always clear when and how to leverage the advantages [for full text, please go …

de, gb (code pays fourni par la source)

xGNN4MI: explainability of graph neural networks in 12-lead electrocardiography for cardiovascular disease classification

M. Mäurer, P. Hempel, Kristin Steinhaus, Hryhorii Chereda et autres

The clinical deployment of artificial intelligence (AI) solutions for assessing cardiovascular disease (CVD) risk in 12-lead electrocardiography (ECG) is hindered by limitations in interpretability and explainability. To address this, we present xGNN4MI, an open-source framework for graph neural networks (GNNs) in ECG …

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