Predictive models are gaining ground across healthcare as more and more hospitals and specialists use them for diagnosis and treatment of cancer and other diseases. But these machine learning tools are still not as accurate or powerful as they could be – and that often boils down to not having enough quality clinical data on which to be trained.

One way to help address the fact that many sample sizes are just too small is to aggregate data from other sources, says Steve Irvine, founder and CEO of integrate.ai. That can be done, while protecting patient privacy, with federated learning techniques, which can open up vast new troves of data for researchers. He explains more in this episode of HIMSSCast.

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Talking points:

• How machine learning models for oncology have evolved in recent years.
• The keys to building a good predictive algorithm.
• Why finding enough quality data to train models is so challenging.
• What federated learning is, and how it can help.
• Opportunities and challenges of embracing a federated learning approach.
• How Irvine sees predictive oncology models evolving in the years ahead.

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