OWKIN FEDERATED LEARNING Why Federated Learning solves the main challenges of Machine Learning in Healthcare As technology advances, machine learning algorithms have become further integrated into every aspect of our lives. Machine learning shows promise in accelerating medical research. We now observe regular predictive modelling and other machine learning techniques to quickly and accurately generate medical insights. That goes, for example, from cancer biomarker identification to patient screening and genetic prediction from imaging. These applications expand researchers’ abilities to make discoveries and help address time & cost obstacles across the healthcare industry. There is a major hurdle preventing the consistent deployment of AI in healthcare at an impactful scale. Machine learning approaches are “data-hungry”. Algorithms need access to large and diverse datasets to train, improve their accuracy, and eliminate bias. However, the traditional infrastructure of modern healthcare systems makes it challenging to organize vast quantities of medical data. GET IN TOUCH Our primary mission is to balance today’s standard approach of centralizing data from multiple centres with critical patient data protection concerns. Strict privacy laws bind software that handles personal data. Healthcare systems must protect personal data at all times. Current standard practices, such as anonymization, may even require removing data that may be critical for medical discoveries. This leaves us with a central challenge: How can we access the volume of data needed to transform healthcare with AI while respecting patient privacy? To access real-world data and larger, more diverse datasets for training AI algorithms, healthcare stakeholders (hospitals, research centres, life science companies) need to start collaborating. But how to do so in a privacy-preserved way? Federated Learning powers the next generation of AI in healthcare Federated learning technology also creates endless possibilities for data scientists and researchers to work on emerging research questions and improve their models, trained across diverse and representative datasets. Models that are more accurate in their predictions also reduce healthcare costs for providers and insurers, overall provide value-based care with better outcomes. How does it work? Federated learning software collaboratively trains machine learning models in a distributed manner without exchanging the underlying data. We dispatch algorithms to different data centres, where they train locally. Only what the algorithm learns at these centres returns to a central location. The improved predictions are then sent to the local datasets to re-train and improve. Federated Learning opens an unprecedented breadth of collaboration in healthcare Let’s take an example applied to healthcare. A data scientist from a large pharmaceutical company needs to test if the machine learning model trained on her in-house data works in the real world. After some negotiations on the contract, she can use federated learning to send the model to data distributed across 5 different hospitals and receive the model’s performance results. She can then use the combined power of all 5 datasets plus her in-house data to train a more accurate and robust model. The model remains secure when travelling to the hospitals, as they cannot access it. In addition, one can use privacy-preserving techniques to prevent leakages between the hospitals and the pharma datasets in both directions. For healthcare applications, this allows predictive models to learn from a large amount of highly curated data. It improves the identification of high-value subgroups of patients for clinical trials, response to treatment prediction, or biomarker selections while respecting data ownership and privacy. OWKIN CONNECT PRODUCT GUIDE Collaborative Federated Learning applications We can also apply this technique with different private companies: we call “collaborative federated learning” or “coopetition”. An example of this is the MELLODDY project. Ten pharmaceutical companies collaborate to train machine learning models for drug discovery based on private and highly sensitive high-content screening datasets. Owkin Connect capabilities help build trust as privacy and security are at the core of the consortium. All of our pharma partners and external security companies audit our platform yearly: Sensitive data and assay- specific models remain securely locked on each pharma’s server. Lower level model components are securely exchanged and trained over the network with secure aggregation. Complex but transparent pre-agreed access arrangements are strictly enforced through distributed ledger technology. According to recent studies, federated learning models can achieve performance levels comparable to ones trained on centrally hosted data sets and, even superior to models that only see isolated single-institutional data. Real World applications of Federated Learning in Healthcare Our federated learning software Owkin Connect is the ideal tool to learn from distributed datasets among consortium partners. It has already taken part in several collaborative research projects around medical research. Working with Owkin and adapting to your consortium needs Owkin can support your consortium in one of two capacities, based on your research topic and the needs of your project. LEARN MORE AI FOR MEDICAL RESEARCH Log in Get in touch Terms of use Privacy Policy Cookie Policy © 2021 OWKIN, INC. All patient data visualized on this website is for illustrative purposes and is a combination of fabricated information and open source databases. 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