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An Algorithm to Detect Low Blood Pressure During Surgery
http://www.hcanews.com/news/an-algorithm-to-detect-low-blood-pressure-during-surgeryAn Algorithm to Detect Low Blood Pressure During Surgery
Ryan Black
JUNE 12, 2018
There’s a lot of theoretical applications for machine learning in healthcare, but almost as often as we hear about those, we see a new study outlining an actual, practical tool made possible by a series of smart algorithms. Adding to that ever-growing pile is a new tool that can predict hypotension—low blood pressure—in surgical patients as soon as 15 minutes before it sets in.
The methodology was built atop nearly 550,000 minutes of surgical arterial waveform recordings from 1,334 patients’ records (which included more than 25,000 instances of hypotension). It required high-fidelity recordings, but from them it could extract more than 3,000 unique features per heartbeat, leading to millions of data points to base the algorithm on, according to the study.
"We are using machine learning to identify which of these individual features, when they happen together and at the same time, predict hypotension," lead researcher Maxime Cannesson, MD, PhD, said in a statement. Cannesson is a professor of anesthesiology and vice chair for perioperative medicine at UCLA Medical Center.
Once developed, the algorithm was validated against more than 33,000 minutes of recordings from over 200 patients’ records, which featured about 1,900 instances of hypotension.
...
Ryan Black
JUNE 12, 2018
There’s a lot of theoretical applications for machine learning in healthcare, but almost as often as we hear about those, we see a new study outlining an actual, practical tool made possible by a series of smart algorithms. Adding to that ever-growing pile is a new tool that can predict hypotension—low blood pressure—in surgical patients as soon as 15 minutes before it sets in.
The methodology was built atop nearly 550,000 minutes of surgical arterial waveform recordings from 1,334 patients’ records (which included more than 25,000 instances of hypotension). It required high-fidelity recordings, but from them it could extract more than 3,000 unique features per heartbeat, leading to millions of data points to base the algorithm on, according to the study.
"We are using machine learning to identify which of these individual features, when they happen together and at the same time, predict hypotension," lead researcher Maxime Cannesson, MD, PhD, said in a statement. Cannesson is a professor of anesthesiology and vice chair for perioperative medicine at UCLA Medical Center.
Once developed, the algorithm was validated against more than 33,000 minutes of recordings from over 200 patients’ records, which featured about 1,900 instances of hypotension.
...
More at link.