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Federated data ecosystems are changing healthcare

Collaborating without sharing data is allowing greater breakthroughs and efficiency
Breakthroughs in healthcare are faster and more reliable with federated data ecosystems. By processing patient data without risking its integrity, data collaboration is safer and more effective than data sharing.
This e-book highlights real-world examples and explains how to implement a federated data ecosystem in pharma and healthcare:

checkmark Benefits of federated data ecosystems in pharma and healthcare
checkmark Examples in drug discovery, clinical trials and commercial growth
checkmark How data collaboration compares with data sharing
checkmark How to get started with federated data ecosystems

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White Paper

Federated Learning on Vertically Distributed Healthcare Data

Joining complementary data sets creates stronger machine learning capabilities. But valuable data containing intellectual property cannot be shared and must adhere to regulatory frameworks. Healthcare companies can use federated learning to work with decentralized data, allowing secure collaboration without sharing data.

In this white paper, you will learn:

checkmark About vertically distributed data
checkmark Workflow of privacy-preserving data science
checkmark Deep-dive into privacy-preserving record linkage
checkmark Deep-dive into federated and privacy-preserving analytics
Case Study

Case Study: Pharma

Learn how to leverage federated and sensitive data at scale and how you can collaborate with your partners to advance and evolve the practice of life sciences.

In this case study you will learn:

checkmark How two pharma companies use protein-ligand interaction data and AI models to predict target binding of novel compounds
checkmark How a pharma company fueld their drug discovery activities by leveraging molecule data from their suppliers
checkmark How an AI-focussed health tech company developed AI models to improve decision-making processes for cancer treatments
Case Study

Case Study: Healthcare

We need to make biomedical data easily accessible and securely usable, to find new medicines and diagnostics, and to establish data-based insights that evolve the practice of medicine.

In this case study you will learn:


checkmark How a MedTech company built up the capability for reliable and strategic data access to thousands of annotated MRI scans from several hospitals
checkmark How a data science team in a pharma company collaborated with hospitals and institutions to securely leverage cancer patient data for AI
checkmark How a genomics company analyzed genomics data from laboratories at scale
White Paper

Federated Learning on Vertically Distributed Healthcare Data

Joining complementary data sets creates stronger machine learning capabilities. But valuable data containing intellectual property cannot be shared and must adhere to regulatory frameworks. Healthcare companies can use federated learning to work with decentralized data, allowing secure collaboration without sharing data.

In this white paper, you will learn:

checkmark About vertically distributed data
checkmark Workflow of privacy-preserving data science
checkmark Deep-dive into privacy-preserving record linkage
checkmark Deep-dive into federated and privacy-preserving analytics