Prof. Dr. Matthias W. Beckmann

Women's Hospital

Projects „digital health“

At the Department of Gynecology and Obstetrics, Universitätsklinikum Erlangen:


DigiOnko is an integrative concept for personalized precision medicine in breast cancer prevention, early detection, therapy and relapse prevention.

digiOnko is an initiative spearheaded by the Bavarian Innovation Alliance against Cancer, which is committed to advancing the field of oncology in Bavaria by catalyzing innovative solutions against cancer. The alliance aspires to elevate the quality of oncological healthcare and fortify the healthcare system’s resilience even in the face of crises such as pandemics. The program’s overarching goal is to utilize insights gleaned from breast cancer research to benefit other types of cancer.

The project aims to achieve the following goals:

  • Digitization of prevention
  • Use of home medical care and diagnostics
  • Implementation of health apps in the care concept4
  • Use of machine learning to analyze existing and collected data



Trusted Ecosystem of Applied Medical Data eXchange

TEAM-X is a consortium of eleven partners and three collaborators from various fields of expertise. Together they develop technical solutions (like the cloud-edge approach within the TEAM-X data-ecosystem) and analyze ethical, legal and social consequences. The consortium is working on strengthening the peoples sovereignty and capabilities concerning personal medical files. TEAM-X is set to boost the digital literacy and innovative strength of the healthcare and nursing sector. TEAM-X is funded by the German Federal Ministry for Economic Affairs and Climate Action.

The Trusted Ecosystem of Applied Medical Data eXchange project is currently implementing two use cases as prototypes. These use cases – namely, digital care platforms and women’s health and palliative medicine – are being developed in compliance with GAIA-X guidelines across two distinct yet interrelated environments in collaboration with the consortium partners at the University Hospital Erlangen.

The project aims to achieve the following goals:

  • Develop digital tools for GAIA-X compliant storage, data exchange, and communication
  • Enhance doctor-patient communication through digital platforms
  • Improve therapy adherence by leveraging digital communication channels
  • Utilize federated machine learning for enhanced data analysis and insights.



To enable all healthcare providers to make the best possible therapeutic decisions at every stage of the patient care continuum, it is imperative that they have a comprehensive view of the complex clinical picture of their patients. Supporting this with innovative IT solutions is a central goal of the MIDIA-Hub project.

The project aims to achieve the following goals:

  • Networking of treatment data from various sources
  • Development of a physician portal
  • Development of a patient portal.


MLWin (abgeschlossen) / COMMITMENT

Maschinelles Lernen mit Wissensgraphen

Combining deep learning techniques with background knowledge and unstructured information can enable the development of predictive systems that provide learning-based decision support. A semantic knowledge graph, for instance, can capture the background information about a patient, while an episodic knowledge graph can describe procedures, diagnoses, and analyses. Subsymbolic knowledge, on the other hand, encompasses radiological images, genetic profiles, and unstructured medical reports.

The project aims to achieve the following goals:

  • Establishment of an algorithm for predicting therapy decisions based on knowledge graphs (MLWin; project already completed)
  • Improvement of physician-patient communication through the use of artificial intelligence (continuation under the COMMITMENT study).



Smart sensors during pregnancy – an integrative concept for digital, preventive care for pregnant women

The SMART Start project is an interdisciplinary research initiative conducted by a team of experts from various fields, including medicine, computer science, ethics, psychology, and health economics. The project is also focused on ensuring clinical usability, social acceptance, stakeholder compliance, and the advancement of sensory technologies, as well as addressing the associated ethical, medical, legal, and economic concerns.

The project aims to achieve the following goals:

  • Enhancing and simplifying the prevention and care of pregnant women
  • Incorporating digital, mobile devices into pregnancy care
  • Utilizing artificial intelligence and machine learning to enhance care.


RESCUER (nur am Rande ein Digitalisierungsprojekt)

RESistance Under Combinatorial Treatment in ER+ and ER- Breast Cancer

As part of the EU/H2020 RESCUER project, our aim is to organize clinical study data in a shareable and annotated database to facilitate reanalysis across studies. The RESCUER consortium consists of fifteen organizations from ten different countries (Belgium, Finland, France, Germany, Israel, Norway, Spain, Sweden, United Kingdom, United States) and gathers all the relevant expertise and facilities to solve the challenge of identifying novel characterization methods for breast cancer drug resistance and new knowledge on effective combinatorial treatments.

The project aims to achieve the following goals:

  • Implementation and harmonization of clinical data from multiple studies
  • Prediction of therapy response and prognosis through big data analysis
  • Development of new clinical studies based on insights gleaned from the data analysis

Related Research Fields:




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