Health
Can AI identify which coronavirus patients are at risk for serious illness?
The international team has designed computer programs that predict with up to 80% accuracy. Coronavirus Patients develop severe respiratory illness.
An artificial intelligence (AI) program developed by researchers in the United States and China is being tested at two hospitals in China with 53 patients diagnosed with Covid-19 in January. The new tool is considered and is currently being tested.
The goal is to help physicians maximize their limited resources by early identifying patients who are likely to need hospital beds and can be sent home for self-care It is to do. In theory, aggressive treatment can be given directly, even if no serious symptoms are first seen.
“Of those with symptoms, 80%-perhaps up to 85%-will have a mild illness. About 15% to 17% have a severe illness and need to be hospitalized. An additional 3% 5% require intensive care, usually due to acute respiratory distress syndrome [ARDS]”Said Dr. Megan Coffee, co-author of the study.
Covid-19 begins calmly with everyone
She is a Clinical Assistant Professor of Infectious Diseases and Immunology at the NYU Grossman School of Medicine in New York City.
ARDS is a potentially fatal condition in which fluid leaks into the lungs and breathing becomes increasingly difficult. According to coffee, at least two-thirds of Covid-19 patients who need treatment in hospital intensive care units develop ARDS, which is “often a fundamental process leading to death.”
However, coffee stated that Covid-19 began mildly with everyone, with cough, fever, upset stomach.
“To develop a very serious disease, a small percentage progress after 5 to 10 days in some patients and require intubation. Who is not always clear,” she says. Was. “Sometimes, someone in their thirties without a medical history has a more serious illness than someone in their seventies who has multiple medical problems.”
According to Coffee, the goal was to develop an artificial intelligence version of the “Master Clinician”. This means a very experienced doctor who treats well-known diseases.
In collaboration with researchers at two hospitals in Wenzhou, China, the NYU team has devised a computer model based on a kind of “predictive analytics” used to predict stock market activity and voting patterns.
A slight increase in liver enzymes
They provided patient-related information, such as lung scan and blood test results, muscle pain and fever patterns, immune response, age and gender.
Surprisingly, researchers have found that factors that most clinicians are likely to focus on, such as lung condition, age, and gender, do not help predict results.
What was that?
The most accurate predictor was a slight elevation of a liver enzyme called alanine aminotransferase (ALT). Deep muscle pain; high levels of hemoglobin, a protein that promotes blood transport of oxygen throughout the body.
“That’s the value of this approach,” Coffee said, “as clinicians, look for things we might not notice.”
The program needs to be validated in a larger population, but she said she could easily roll out if similar precision was found in future tests.
Tested on very small samples
The tool could prove “very useful,” said Dr. Maria Luisa Alcaide, a fellow at the American Society for Infectious Diseases, who reviewed the findings.
“What’s happening with Covid-19 is that the number of cases has increased so much that ICUs in some hospitals have been overwhelmed,” she said. “And for reasons that are not well understood, not all people who become very ill will fit the profile of older people with underlying illness.”
Arcade, an associate professor of infectious diseases at the University of Miami, said that the better doctors can predict who can predict, the more accurate they can track them and their care.
However, she noted that the method has only been tested on a very small sample of patients and hundreds of thousands are currently infected.
“This small sample is unlikely to be representative of all Covid-19 patients,” Alcaide said. Some of these predictors may prove to be important. But I don’t know yet. Other markers may be more important. Therefore, this really needs to be verified in more patients. “
Findings are reported online in journal issues Computer, material, continuum.
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