“The results demonstrate that a machine learning algorithm can be trained, with large data sets of high-fidelity arterial waveforms, to predict hypotension in surgical patients’ records,” conclude the authors.
“It is the first time machine learning and computer science techniques have been applied to complex physiological signals obtained during surgery,” according to lead researcher Maxime Cannesson, MD, vice chair for perioperative medicine and professor of anesthesiology at UCLA Medical Center. “Physicians haven’t had a way to predict hypotension during surgery, so they have to be reactive and treat it immediately without any prior warning. Being able to predict hypotension would allow physicians to be proactive instead of reactive.”
While future studies are needed to evaluate the real-time value of such algorithms in a broader set of clinical conditions and patients, Cannesson contends that the research “opens the door to the application of these techniques to many other physiological signals, such as EKG for cardiac arrhythmia prediction or EEG for brain function” and “could lead to a whole new field of investigation in clinical and physiological sciences and reshape our understanding of human physiology.”