The research team, writing in the Journal of Medical Internet Research, says late or under-identification of a blood clot in one or more arteries to the lungs seriously threatens patients’ lives and is “a major challenge confronting modern medicine.”
They analyzed data available before emergency department admission for 2,568 patients with PE and 52,598 patients in a much larger “control group” in which just 4 percent had PE.
Results from the study showed the algorithm could accurately identify and predict which patients were at high risk of PE upon hospital admission, allowing doctors to diagnose and begin treatment early.
“Early and timely diagnosis of pulmonary embolism is challenging, yet crucial, due to the condition’s high rate of mortality and morbidity,” said Prof. Gad Segal, head of the Sheba Education Authority, who conducted the study together with computational development researchers at Ben Gurion University in Beersheva, southern Israel.
“This study highlights the enormous potential of machine learning tools to support innovation in diagnostics. Even though the model only used data available from patients on arrival to the ER, it was still able to predict with high accuracy the likelihood of a patient developing PE, a crucial advancement for patient care and outcomes.”
Produced in association with ISRAEL21c
