AI: This COVID machine-learning tool helps swamped hospitals pick the right treatment

A Barcelona hospital's artificial-intelligence tool analyzes trillions of pieces of data to predict likely patient outcomes.
Written by Anna Solana, Contributor

Spain has been one the European states worst hit by the COVID-19 pandemic, with more than 1.7 million detected cases. Despite the second wave of infections that has hit the country over the past few months, the Hospital Clinic in Barcelona has succeeded in halving mortality among its coronavirus patients using artificial intelligence.

The Catalan hospital has developed a machine-learning tool that can predict when a COVID patient will deteriorate and how to customize that individual's treatment to avoid the worst outcome.

"When you have a sole patient who's in a critical state, you can take special care of them. But when they are 700 of them, you need this kind of tool," says Carol Garcia-Vidal, a physician specialized in infectious diseases and IDIBAPS researcher who has led the development of the tool.

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Before the pandemic, the hospital had already been working on software to turn variable data into an analyzable form. So when the hospital started to receive COVID patients in March, it put the system to work analyzing three trillion pieces of structured and anonymized data from 2,000 patients.

The goal was to train it to recognize patterns and check what treatments were the most effective for each patient and when they should be administered.

That work underlined to Garcia-Vidal and her team that the virus doesn't manifest itself in the same way for everyone. "There are patients with an inflammatory response, patients with coagulopathies and patients who develop super infections," García-Vidal tells ZDNet. Each group needs different drugs and thus a personalized treatment.

Thanks to an EIT Health grant, the AI system has been developed into a real-time dashboard display on physicians' computers that has become one of their everyday tools. Under the supervision of an epidemiologist, the tool enables patients to be classified and offered a more personalized treatment.

"Nobody has done this before," says García-Vidal, who says the researchers recently added two more patterns to the system to include the patients who are stable and can leave the hospital, thus freeing a bed, and those patients who are more likely to die. The predictions are 90% accurate.

"It's very useful for physicians with less experience and those who have a specialty that's nothing to do with COVID, such as gynecologists or traumatologists," she says. As in many countries, doctors from all specialist areas were called in to treat patients during the first wave of the pandemic.

The system is also being used during the current second wave because, according to García-Vidal, the number of patients in intensive care in Catalan hospitals has jumped. The plan is to make the tool available to other hospitals.

Meanwhile, the Barcelona Supercomputing Center (BSC) is also analyzing a set of data corresponding to 3,000 medical cases generated by the Hospital Clínic during the acute phase of the pandemic in March.

The aim is to develop a model based on deep-learning neural networks that will look for common patterns and generate predictions on the evolution of symptoms. The objective is to know whether a patient is likely to need a ventilator system or be directly sent to intensive care.

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Some data – such as age, sex, vital signs and medication given – is structured but other data isn't, because it consists of text written in natural language in the form of, for example, hospital discharge and radiology reports, BSC researcher Marta Villegas explains.

Supercomputing brings the computational capacity and power to extract essential information from these reports and train models based on neural networks to predict the evolution of the disease as well as the response to treatments given the previous conditions of the patients.

This approach, based on natural language processing, is also being tested at a hospital in Madrid. 

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