COVID-19 doesn’t create cookie cutter infections. Some people have extremely mild cases while others find themselves fighting for their lives.
Clinicians are working with limited resources against a disease that is very hard to predict. Knowing which patients are most likely to develop severe cases could help guide clinicians during this pandemic.
We are two researchers at New York University that study predictive analytics and infectious diseases. In early January, we realized that it was very possible the new coronavirus in China was going to make its way to New York and we wanted to develop a tool to help clinicians deal with the incoming surge of cases. We thought predictive analytics—a form of artificial intelligence—would be a good technology for this job.
In a general sense, this type of AI looks at existing data to find patterns and then uses those patterns to make predictions about the future. Using data from 53 COVID-19 cases in January and February, we developed a group of algorithms to determine which mildly ill patients were likely become severely ill.
Our experimental tool helped predict which people were going to get the most sick. In doing so, it also found some unexpected early clinical signs that predict severe cases of COVID-19.
The algorithms we designed were trained on a small dataset and at this point are only a proof-of-concept tool, but with more data we believe later versions could be extremely helpful to medical professionals.
How we did the work
To build this tool, we first needed data. We teamed up with an infectious disease specialist, Xiangao Jiang, in Wenzhou, Zhejiang, China. When we started working on this in early January, Wenzhou had the largest outbreak outside of Hubei, of which Wuhan is the capital. Between January and February,