Epidemiology Centre (ECU)

Expertise in core epidemiology

We support you with an interdisciplinary team specialising in biometrics, epidemiology, medical informatics, health sciences/public health and paediatrics, with complementary specialist expertise currently available at nine locations.

Expert reports

NUM4Rare Project

Brief description: Establishment of a German registry infrastructure for rare diseases: linking disease-specific registries, clinical data and PROMs from patients with rare diseases

Role of Num-MB: Methodological advice on the collection, harmonisation and use of PROMs within NUKLEUS

num4rare

 

NUM external partners & collaborations

Outcomes from external healthcare-related data (eVeDa)

NUM4Rare, NUMlinkTREAT sub-project

Brief description:
Establishment of a German register infrastructure for rare diseases; linking the TREAT register to NUM4Rare.

Role of NUM-MB:
Advice on linking external register data from the TREATgermany medical register with health insurance data.

Num4rare

 

NAPKON-POP follow-up

Role of NUM-MB:
Methodological advice on linking with health insurance data

 

NUM-MB EC

NUM-MB role:
Advice on adapting patient information, consent and secondary use documents regarding the linkage of external data (health insurance funds, cancer registries, ePA)

Paediatrics-specific methodological guidance for researchers within the NUM

  • Members of the interdisciplinary team provide support with:
    - individual planning
    - implementation and
    - evaluation of paediatric clinical epidemiological studies
  • The team also creates and develops:
    - tutorials and webinars on paediatric-specific issues, as well as
    - health apps for use in paediatric therapy and care

Methodology Toolbox:

A comprehensive toolkit of methods and instruments, comprising internationally standardised and established screening and diagnostic tools for assessing the health and clinical conditions of children and young people.

  • Generative AI enables effective, resource-efficient structuring and classification of unstructured data
  • Areas of application: data imputation, curation and augmentation; process automation; matching different data sets; metadata corrections
  • Machine Learning enables the identification of non-linear and complex associations in complex models and, provided there is sufficient data, can be applied without prior knowledge (‘hypothesis-free’).
  • Areas of application: classification and predictive models, causal inference
  • Practical advice on the use of the latest Machine Learning and generative AI methods in relation to clinical and clinical-epidemiological data
  • Assistance in identifying suitable project partners

Outcomes of Clinical Data Science

Project Brief description Role NUM-MB Link
Systematic Review with LLMs Investigation into the use of Large Language Models (LLMs) to partially automate systematic reviews Expertise CDS  
GENIOBA Support for the conduct of a systematic review (“Systematic Review on the Gender Gap in the Care of Patients with Impulse Control Disorders”) by Ruhr University Bochum using several Large Language Models (LLMs) Expertise: CDS  
Dr LLM Investigation into the diagnostic potential of Large Language Models (LLMs) to support clinical differential diagnoses in infectious diseases Expertise CDS  
Mignomat LLM-based process trust agency to improve data minimisation in the process automation of agent-based workflows Expertise CDS