AI can determine if you will die from Covid-19 with 90% accuracy

Artificial intelligence is everywhere, and now a group of developers have created AI software that can tell if it is possible to die from Covid-19 using health data.

Researchers at the University of Copenhagen fed a computer program with health data from 3,944 Danish patients with COVID-19, as well as any underlying conditions.

They then instructed him to look for patterns in a patient’s previous illness to determine the risk factors and potential outcome of Covid-19 and found that BMI, age and being male were the biggest risk factors when it came to probability. to die.

The results show that AI can, with up to 90 percent certainty, determine if an uninfected person will die of the disease if they are unlucky enough to catch it.

The results of the new tool could help health officials determine who should be in the front line for a limited amount of vaccines, said lead author Mads Nielsen.

Once hospitalized with Covid-19, the computer software can predict with 80% accuracy whether the person will need a respirator, the team found.

Certain diseases and health factors have a greater influence on whether a patient reaches a respiratory tract than others after being infected, according to the study.

In order of priority they are: BMI, age, high blood pressure, being men, neurological diseases, COPD, asthma, diabetes and heart disease.

The group most at risk of dying from coronavirus if they contract the disease are old men, white and fat, with high blood pressure, according to researchers.

They found that for diagnosed patients, age and BMI were among the most relevant characteristics for predicting hospitalization and ventilator treatment.

High blood pressure – high blood pressure – was the most important feature for predicting ICU admission and, indeed, an important feature for all models.

For patients who had to be hospitalized, the most relevant factors of disease progression were age, BMI, hypertension and the presence of dementia.

“We started working on hospital care models because during the first wave, they feared they did not have enough respirators for intensive care patients,” said Professor Nielsen.

“Our results show, surprisingly, that age and BMI are the most decisive parameters for how severely a person will be affected by COVID-19,” he said.

“But the likelihood of dying or getting on a respirator is also high if you’re a man, have high blood pressure, or have a neurological disease,” explains Mads Nielsen.

For hospitalized patients requiring ICU hospitalization compared to hospitalized patients without ICU admission, only males, BMI, dementia, and hypertension differed between patients.

The team found that, in addition, intensive care patients were more likely to be smokers, the elderly and men.

Those who died of the disease were also more likely to suffer from hypertension, diabetes, heart disease, heart failure, arrhythmias, stroke, COPD or asthma, osteoporosis, dementia, mental disorders, neurological diseases, cancer, chronic renal failure and the use of dialysis.

“For those affected by one or more of these parameters, we found that it might make sense to move them to the vaccination queue, to avoid any risk of inflection and, finally, to get on a respirator. Says Nielsen.

The team behind the study is now working to update its model with new data from the latest wave of coronavirus in Denmark.

They hope that artificial intelligence will soon be able to help hospitals in the country by continuously predicting the need for respirators.

“We are working towards a goal that we should be able to anticipate the need for respirators five days in advance, giving the computer access to health data for all COVID positives in the region,” says Mads Nielsen.

“The computer will never be able to replace a doctor’s evaluation, but it can help doctors and hospitals see many COVID-19-infected patients simultaneously and set ongoing priorities.”

The findings were published in the journal Scientific Reports.