Algorithm makes it easier to detect sepsis
Each year, nearly 8,000 Swedes die of sepsis, commonly known as blood poisoning. Inger Persson and her team have developed the NAVOY Sepsis algorithm to aid healthcare professionals by enabling the early detection and treatment of sepsis, a disease that is difficult to diagnose and often progresses very quickly. The goal: to save more lives.
The idea originally came from one of Persson’s former colleagues in the pharmaceutical industry.
“He contacted me and asked if we could put our heads together and see if we could come up with something that enabled us to use the vast amounts of data available in healthcare,” she says.
They started by bouncing around ideas about which diseases could be predicted and where this would be most useful. They settled on sepsis early on, as it is a condition that is difficult to diagnose, has a high mortality rate, and lacks tools for seeing which patients are at risk of developing the disease.
“We saw that there was an opportunity to do something here, based entirely on data,” says Inger Persson, who has been researching and teaching at the Department of Statistics at Uppsala University for the past ten years.
Inger Persson and her colleagues collaborate with sepsis researchers and combine research with business development. The machine learning algorithm helps the healthcare detect sepsis early and is a decision support for healthcare professionals.
Find out more
See the film: Sepsis Prediction Algorithm - Assisted Intelligence for Intensive Care
Read the article: A Machine Learning Sepsis Prediction Algorithm for Intended Intensive Care Unit Use (NAVOY Sepsis): Proof-of-Concept Study
Inger Persson, Senior Lecturer at the Department of Statistics, Uppsala University.