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AI identifies pain levels from patient data A research team led by Northwestern University faculty and alumni has found it’s possible to understand a patient’s pain level by examining data from vital signs. In a new study, the team developed and applied artificial intelligence (AI), or machine-learning, algorithms to physiological data including respiratory rate, blood pressure, heart rate, body temperature and oxygen levels from patients with chronic pain from sickle cell disease. Not only did the researchers’ approach outperform baseline models to estimate subjective pain levels, it also detected changes in pain and atypical pain fluctuations. The study was published March 11 in the journal PLOS Computational Biology. This is the first paper to demonstrate that machine learning can be used to find clues to pain hidden within data from patients’ vital signs.

Fan yangTanvi banerjeeDaniel abramsNirmish shahMark panaggioMccormick school of engineeringWright state universityJohns hopkins university applied physics laboratoryDuke medical centerNational institutes of healthDuke universityNorthwestern universityMccormick schoolJohns hopkins university applied physicsNational institutesவிசிறி யாங்