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DANVILLE, Va. â At 6:18 a.m. this morning, public safety personnel responded to a crash on Route 29 near the Route 58 interchange in Danville.
Officers found a tractor trailer truck lying on its side in the highway. The investigation showed that the vehicle was traveling west on Route 58, took the off ramp to travel south on Route 29. The driver lost control and overturned across the southbound lane of Route 29.Â
The driver was identified as John Fielden Clark, 77, of Suffolk, Virginia. Clark had to be extricated from the truck and was taken to the Emergency Room at SOVAH of Danville. He was later flown to Duke Medical Center for treatment.
Dr. Rex L. Mahnensmith, MD a 2021
“Patient Preferred Physician” representing the state of Connecticut.
With over three decades of achievements in his field, Dr. Mahnensmith treats patients via On–Call Means for acute care and serves as Primary Care Physician for a growing group of patients with the Continuum Medical Group of DOCS of CT. Drawing on his many years of expertise, Dr. Mahnenesmith provides knowledge and treatment of various disorders including asthma, diabetes, hypertension, nephrolithiasis, kidney diseases, and associated chronic disabilities. Moreover, Dr. Mahnensmith prides himself on being able to provide holistic care to many of his patients who have a complex and disabling illness, and who are not able to receive the holistic care they require because of their station in life.
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.