Labels:
machine-learning secure-learning multi-party-computation

Description

Identifying heart failure patients at high risk using Multi-Party Computation.

Machine learning algorithms are widely used to improve health care, for example to identify risk factors for a disease. Results can be used for the development of new treatments. For training these algorithms, a lot of data are needed, often divided over different data sources. In practice, however, combining these data sources is both legally and technically challenging. When scientific researchers want to use personal retractable medical data for machine learning algorithms, this needs to be in line with the General Data Protection Regulation (GDPR). Local data can often be used for scientific research purposes, however it becomes challenging when data from different sources needs to be combined. This is because, first of all, the GDPR focuses on using as little data as possible, while machine learning thrives on large datasets. Secondly, consent from the patient is often needed, which is time-consuming and causes practical problems, for example because the hospital is no longer in contact with the patients. This is where Secure Multi-Party Computation (MPC) solutions come in. Although we do not use real patient’s data, the set-up of our MPC solution is inspired by the following real-life situation. In Rotterdam, there is a group of patients that both is insured by insurance company Zilveren Kruis and took part in a program by hospital Erasmus MC. On one side, Erasmus MC has data on the lifestyle of these patients, for example their exercising behaviour. On the other side, Zilveren Kruis has data on different attributes such as hospitalization days and health care usage outside the hospital. These datasets, once combined, could be used to train a prediction model that identifies high risk heart failure patients. However, concerns about privacy and consent (to name a few) mean that these parties cannot simply share their data to allow for a straightforward analysis. That is why, in 2018, TNO, together with Erasmus MC and Zilveren Kruis, started a pilot within the H2020 Project BigMedilytics to develop a secure algorithm to predict the number of hospitalization days for heart failure patients. Note that we only use synthetic (though realistic) data during this MPC demo. With this data, we use MPC to calculate the relation between the number of hospitalization days and possibly important factors such as lifestyle.

Contact

  • Alex Sangers, Project Manager, TNO, e-mail: alex.sangers@tno.nl