New artificial intelligence technology will make it possible to identify patients who are at risk of serious illness as a result of blood infections.
Researchers at Tel Aviv University trained an AI program to study the electronic medical records of about 8,000 patients at Tel Aviv’s Ichilov Hospital who were found to be positive for blood infections. After studying each patient’s data and medical history, the program was able to automatically identify medical files’ risk factors with an accuracy of 82%.
These records included demographic data, blood test results, medical history and diagnosis.
The results of the study were published in the journal Scientific Reports.
According to the researchers, in the future this model could even serve as an early warning system for doctors, by enabling them to rank patients based on their risk of serious disease.
Behind this groundbreaking research with the potential to save many lives are students Yazeed Zoabi and Dan Lahav from the laboratory of Noam Shomron, PhD, of Tel Aviv University’s Sackler Faculty of Medicine, in collaboration with Ahuva Weiss Meilik, PhD, head of the I-Medata AI Center at Ichilov Hospital, Amos Adler, MD, and Orli Kehat, PhD.
The researchers explain that blood infections are one of the leading causes of morbidity and mortality in the world, so it is very important to identify the risk factors for developing serious illness at the early stage of infection with a bacterium or fungus.
“We worked with the medical files of about 8,000 Ichilov Hospital patients who were found to be positive for blood infections between the years 2014 and 2020, during their hospitalization and up to 30 days after, whether the patient died or not,” says Shomron. “We entered the medical files into software based on artificial intelligence; we wanted to see if the AI would identify patterns of information in the files that would allow us to automatically predict which patients would develop serious illness, or even death, as a result of the infection.”
After the researchers entered the patient’s data, the algorithm knew how to predict the course of the disease, which suggests that in the future it will be possible to rank patients in terms of the danger posed to their health – ahead of time.
“Using artificial intelligence, the algorithm was able to find patterns that surprised us, parameters in the blood that we hadn’t even thought about taking into account,” says Shomron. “We are now working with medical staff to understand how this information can be used to rank patients in terms of the severity of the infection. We can use the software to help doctors detect the patients who are at maximum risk.”
Since the study’s success, Ramot, Tel Aviv University’s technology transfer company, is working to register a global patent for the technology.
