Perioperative Autoregulation Collaboration: The Path Forward to Precision Medicine
October 4, 2022
Dr. Ryan Melvin
- Principal data scientist in the Department of Anesthesiology and Perioperative Medicine at UAB
Dr. Ryan Godwin
- Data Scientist
- Anesthesiology & Radiology
- The University of Alabama at Birmingham
Dr. Dan Berkowitz
- Chair of the Department of Anesthesiology and Perioperative Medicine at UAB
Can artificial/augmented intelligence (AI) and machine learning be employed to derive more intelligence from data? Is there a path forward to using that data to optimize care delivery for individual patients or even predict health events?
Those are just two of the questions that the periop medicine and data science teams at UAB set out to answer. Listen to Dr. Berkowitz, Dr. Melvin, and Dr. Godwin as they explain how they achieved their goals by accessing real-time continuous data and collecting data for retrospective analysis.
Learn more about:
- Accessing secure, waveform-enabled medical device integration from non-networked devices such as near-infrared spectroscopy (NIRS), ventilators, anesthesia machines, extracorporeal membrane oxygenation (ECMO), electroencephalograms (EEG), and more
- Analyzing several alterations during different types of cardiac procedures as well as during the different stages of those procedures and how the results could then be correlated with the various types of available management and medications
- The cardiac analysis required taking a large amount of time-series data consisting of hours of surgery samples, collected at 120 samples per second, and transforming the data into simple, illustrative and useful curves. Thus, data is transformed into useful and actionable information
- Combining and transforming two or more complex signals to create a brand-new, more complete view of the patient’s status
PREVIEW CLIPS FROM THE WEBINAR
Click on a video below to watch.