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Quick Start by Role

PhD students & Postdocs — Start with Data Types and Use Cases. Important: Consult your data protection officer before processing personal health data.

PIs & Group Leaders — Review Metadata Standards (DICOM, HL7 FHIR, CDISC) and Repositories. See also Data Management Plans.

Data Stewards — Key cross-references: Repositories, Metadata, privacy regulations (GDPR, BDSG).

What is Medical & Clinical Data Science?

Medical and clinical data science covers the collection, management, and analysis of health-related data — from medical images and electronic health records to clinical trial results and biosensor measurements. These data require special attention to privacy regulations, interoperability standards, and ethical considerations that distinguish them from most other research domains.

The Need for Data Management in Medical & Clinical Research​

Healthcare data management faces uniquely stringent requirements:

  • Privacy and ethics — Patient data is subject to GDPR, the German Bundesdatenschutzgesetz (BDSG), and institutional ethics board requirements
  • Anonymization and pseudonymization — Data must be de-identified before research use while preserving analytical utility
  • Interoperability — Hospitals, clinics, and research institutions use different systems that must communicate through standardized formats
  • Data volume and variety — A single hospital generates terabytes of imaging, lab, and clinical data per year in dozens of formats
  • Longitudinal data — Patient records span years or decades and must be linked reliably
  • Regulatory compliance — Clinical trials follow strict protocols (GCP) with mandated data retention periods

Key Application Areas​

  • Medical imaging — Radiology (CT, MRI, X-ray), pathology (whole slide images), ultrasound
  • Clinical trials — Randomized controlled trials, observational studies, registry data
  • Electronic health records (EHR) — Structured and unstructured clinical documentation
  • Precision medicine — Integrating genomic, clinical, and lifestyle data for personalized treatment
  • Epidemiology — Population health studies, disease surveillance, outbreak modeling
  • Wearable and biosensor data — Continuous monitoring, activity tracking, remote patient monitoring

Common Tools and Software​

Medical Imaging​

  • 3D Slicer — Open-source platform for medical image analysis
  • ITK / SimpleITK — Image processing libraries
  • MONAI (Python) — Deep learning framework for medical imaging
  • OsiriX / Horos — DICOM viewers

Clinical Data Management​

  • REDCap — Secure web application for building and managing clinical databases
  • OHDSI / OMOP CDM — Standardized clinical data model for observational research
  • OpenClinica — Open-source clinical trial management

Data Analysis​

  • Python (pandas, scikit-learn, lifelines) — Statistical analysis and machine learning
  • R (survival, caret, ggplot2) — Biostatistics and visualization
  • SPSS / SAS — Traditional clinical biostatistics software

Privacy and De-identification​

  • ARX — Open-source data anonymization tool
  • Microsoft Presidio — PII detection and anonymization
  • Federated learning frameworks — Train models without sharing raw data

Infrastructure and Support​

Researchers working with medical data at TUM can access:

  • TUM University Hospital (MRI) — Clinical data infrastructure and biobanks
  • LRZ Linux Cluster — For medical image analysis and ML model training
  • LRZ AI Systems — GPU clusters for deep learning on imaging data

Getting Started​

If you're new to medical data science:

  1. Explore our resources — Check out the Data Types and Use Cases
  2. Understand regulations — Familiarize yourself with GDPR and institutional ethics requirements
  3. Get support — Contact rdhub@mdsi.tum.de for data management guidance