A5080057671- Privacy-Preserving Technologies in Data
- Gene expression and cancer classification
- Artificial Intelligence in Healthcare and Education
- Disaster Response and Management
- Cancer Genomics and Diagnostics
- Health Systems, Economic Evaluations, Quality of Life
- Mobile Crowdsensing and Crowdsourcing
- COVID-19 diagnosis using AI
- Knee injuries and reconstruction techniques
- Cryptography and Data Security
- Statistical Methods in Clinical Trials
- Ethics and Social Impacts of AI
- Explainable Artificial Intelligence (XAI)
- Health and Medical Studies
- Stochastic Gradient Optimization Techniques
- Osteoarthritis Treatment and Mechanisms
- Medical Malpractice and Liability Issues
- Total Knee Arthroplasty Outcomes
- Radiomics and Machine Learning in Medical Imaging
- Ethics in Clinical Research
- Single-cell and spatial transcriptomics
- Risk and Safety Analysis
Universität Hamburg
2021-2025
University Medical Center Hamburg-Eppendorf
2024
Helmholtz Zentrum München
2024
WellSpan Health
2023
The collection, storage, and analysis of large data sets are relevant in many sectors. Especially the medical field, processing patient promises great progress personalized health care. However, it is strictly regulated, such as by General Data Protection Regulation (GDPR). These regulations mandate strict security protection and, thus, create major challenges for collecting using sets. Technologies federated learning (FL), especially paired with differential privacy (DP) secure multiparty...
Background Machine learning and artificial intelligence have shown promising results in many areas are driven by the increasing amount of available data. However, these data often distributed across different institutions cannot be easily shared owing to strict privacy regulations. Federated (FL) allows training machine models without sharing sensitive In addition, implementation is time-consuming requires advanced programming skills complex technical infrastructures. Objective Various tools...
Abstract Background Knee osteoarthritis (OA) is a common chronic condition that impairs mobility and diminishes quality of life. Despite the proven benefits exercise therapy patient education in managing OA pain functional limitations, these strategies are often underused. To motivate enhance engagement, personalized outcome prediction models can be used. However, accuracy existing predicting changes knee outcomes remains insufficiently examined. Objective This study aims to validate...
Clinical time-to-event studies are dependent on large sample sizes, often not available at a single institution. However, this is countered by the fact that, particularly in medical field, individual institutions legally unable to share their data, as data subject strong privacy protection due its particular sensitivity. But collection, and especially aggregation into centralized datasets, also fraught with substantial legal risks outright unlawful. Existing solutions using federated...
<title>Abstract</title> scRNA-seq data from clinical samples are prone to batch effects, while hospitals hesitant share their for centralized analysis, including effect correction, due the privacy sensitivity of human genomic data. We present FedscGen, a novel privacy-aware federated method based on generative integration approach scGen. FedscGen presents two workflows training and correction effects with inclusion new studies. benchmark scGen using eight datasets nine metrics demonstrate...
Quantitative mass spectrometry has revolutionized proteomics by enabling simultaneous quantification of thousands proteins. Pooling patient-derived data from multiple institutions enhances statistical power but raises significant privacy concerns. Here we introduce FedProt, the first privacy-preserving tool for collaborative differential protein abundance analysis distributed data, which utilizes federated learning and additive secret sharing. In absence a multicenter dataset evaluation,...
<sec> <title>BACKGROUND</title> The collection, storage, and analysis of large data sets are relevant in many sectors. Especially the medical field, processing patient promises great progress personalized health care. However, it is strictly regulated, such as by General Data Protection Regulation (GDPR). These regulations mandate strict security protection and, thus, create major challenges for collecting using sets. Technologies federated learning (FL), especially paired with differential...
<sec> <title>BACKGROUND</title> Machine learning and artificial intelligence have shown promising results in many areas are driven by the increasing amount of available data. However, these data often distributed across different institutions cannot be easily shared owing to strict privacy regulations. Federated (FL) allows training machine models without sharing sensitive In addition, implementation is time-consuming requires advanced programming skills complex technical infrastructures....