Medical

This project aims to produce methods, processes, and architectures applicable to a variety of scientific computing domains that enables querying, machine learning, and analysis of data while protecting against releasing sensitive information beyond pre-defined bounds. It is supported by LBNL CSR funds and is led by Sean Peisert.

We have defined a Medical Science DMZ as a method that allows data flows at scale while simultaneously addressing the HIPAA Security Rule and related regulations governing biomedical data and appropriately managing risk.

This project is developing a system-based workflow to securely acquire wireless data from mechanical ventilators in critical care environments, and leverage scalable web-based analytic platforms to advance data analytics and visualization of issues surrounding patients with respiratory failure.