A group of researchers from the College of Minnesota has utilized a Natural Language Processing (NLP) approach to effectively predict the required treatment and medical help for 22,529 people involved in car crashes that were then transferred by Emergency Medical Service (EMS) personnel to trauma facilities in Minnesota.
In 2016, research by the Nationwide Academies of Sciences, Engineering, and Drugs, showed that almost 20% of medical damage deaths were preventable provided they receive appropriate medical care earlier. The response time of EMS can allow medical professionals to prevent such deaths therefore the researches tried to bridge the gap between EMS response and proper medical care using AI.
Two trauma surgeons reviewed a random collection of affected person data and decided therapy. They stated that the algorithms classified the appropriate medical care, in line with medical professionals, with excessive accuracy.
The second research predicted the survival of liver transplant patients using Random Forest and AdaBoost with great accuracy as well.
The researches state that their models are so that they can help the clinicians in their decision-making by providing them with quantitative knowledge.
You can read the full article here: MedicalXpress
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