2 research outputs found
Preserving Patient Privacy while Training a Predictive Model of In-hospital Mortality
Machine learning models can be used for pattern recognition in medical data
in order to improve patient outcomes, such as the prediction of in-hospital
mortality. Deep learning models, in particular, require large amounts of data
for model training. However, the data is often collected at different hospitals
and sharing is restricted due to patient privacy concerns. In this paper, we
aimed to demonstrate the potential of distributed training in achieving
state-of-the-art performance while maintaining data privacy. Our results show
that training the model in the federated learning framework leads to comparable
performance to the traditional centralised setting. We also suggest several
considerations for the success of such frameworks in future work.Comment: AI for Social Good Workshop, Neurips 2019, Vancouver, Canad
Federated Learning for Healthcare Informatics
With the rapid development of computer software and hardware technologies,
more and more healthcare data are becoming readily available from clinical
institutions, patients, insurance companies and pharmaceutical industries,
among others. This access provides an unprecedented opportunity for data
science technologies to derive data-driven insights and improve the quality of
care delivery. Healthcare data, however, are usually fragmented and private
making it difficult to generate robust results across populations. For example,
different hospitals own the electronic health records (EHR) of different
patient populations and these records are difficult to share across hospitals
because of their sensitive nature. This creates a big barrier for developing
effective analytical approaches that are generalizable, which need diverse,
"big data". Federated learning, a mechanism of training a shared global model
with a central server while keeping all the sensitive data in local
institutions where the data belong, provides great promise to connect the
fragmented healthcare data sources with privacy-preservation. The goal of this
survey is to provide a review for federated learning technologies, particularly
within the biomedical space. In particular, we summarize the general solutions
to the statistical challenges, system challenges and privacy issues in
federated learning, and point out the implications and potentials in
healthcare.Comment: 18 page