Lead Data Scientist at T and T Consulting Services, Inc., Nitin Kumar, has written a guest blog post discussing the value and potential impact of federated learning in the healthcare field. In particular, it emphasizes the potential for applying the technology to quicker diagnoses and decision-making in heart stroke patients.
Currently, strokes are the fifth leading cause of death and a leading cause of disability in the US, according to the American Stroke Association. On average, diagnosing a stroke can take between 30 minutes to an hour, with delays in crowded emergency departments. Swift, accurate diagnosis is required to reduce the risk of further brain damage.
Machine learning can assist in speeding up the diagnosis process, however, patient data is often isolated in different healthcare systems and organizations due to legal restrictions and patient privacy concerns. This restricts international research teams from working together on diverse datasets.
Federated learning (FL) could provide a solution to this problem. FL is a decentralized form of machine learning, where the model is shared between organizations for training on different data subsets. It can reduce cybersecurity requirements and the need for data engineering pipelines for data across different locations, as well as ensuring data privacy.
FL has been tested in medical sub-fields for use cases such as patient similarity learning, patient representation learning, phenotyping, and predictive modeling, showing promise for addressing challenges within the healthcare field.
Starting with FL requires choosing from many high-quality datasets. For example, datasets with brain images include ABIDE, ADNI, RSNA Brain CT, BraTS, UK BioBank, and IXI. For heart images, choices include ACDC and M&M Cardiac Segmentation Challenge.
A number of tools and libraries have been developed to help with the implementation of FL, with many resources and frameworks now available to support the process, including PySyft, FedML, Flower, OpenFL, FATE, TensorFlow Federated, and NVFlare.
However, FL also has its own challenges, particularly where data privacy and security are concerned. These, however, are straightforward to address, with solutions available to deal with hurdles such as ensuring data quality and uniformity, and achieving model precision.
In conclusion, FL holds significant potential for legacy healthcare data analytics and intelligence, and is simple to implement a cloud-native solution with AWS services.
