Researchers have published one of the first studies using a Machine Learning (ML) technique called “federated learning” to examine electronic health records to better predict how COVID-19 patients will progress.
The study, published in the Journal of Medical Internet Research – Medical Informatics, indicates that the emerging technique holds promise to create more robust machine learning models that extend beyond a single health system without compromising patient privacy.
These models, in turn, can help triage patients and improve the quality of their care. “Machine Learning models in health care often require diverse and large-scale data to be robust and translatable outside the patient population they were trained on,” said co-author Benjamin Glicksberg, Assistant Professor at Mount Sinai.
Federated learning is a technique that trains an algorithm across multiple devices or servers holding local data samples but avoids clinical data aggregation, which is undesirable for reasons including patient privacy issues.
For the study, the researchers implemented and assessed federated learning models using data from electronic health records at five separate hospitals within the Health System to predict mortality in COVID-19 patients.
They compared the performance of a federated model against ones built using data from each hospital separately, referred to as local models.
After training their models on a federated network and testing the data of local models at each hospital, the researchers found the federated models demonstrated enhanced predictive power and outperformed local models at most of the hospitals.
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