Healio reports that a combination of unsupervised and supervised machine learning algorithms may be able to assist clinicians in identifying patients undergoing total knee arthroplasty who are more likely to have pain that is difficult to control, according to an HSS study presented at the 50^th Annual Regional Anesthesiology and Acute Pain Medicine Meeting.

“By identifying who is at risk for difficult to control pain, we will be better able to tailor individualized care for these patients through targeted patient education and prehabilitation before surgery as well as more optimized pain control after surgery,” Justin Chew, MD, PhD, regional anesthesiology and acute pain medicine fellow in the department of anesthesiology at HSS explained about data. “Controlling pain in the immediate postoperative period could prevent acute pain from transitioning into chronic pain, which would ultimately defeat the purpose of the surgery – to relieve pain and improve physical function.”

Read the full article at healio.com.