A nursing faculty-led interdisciplinary team has developed a personalized risk prediction tool for venous thromboembolism (VTE) in patients that could lead to better treatment.
Nursing Assistant Professor Jung In Park, who led the team, published “Personalized Risk Prediction for 30-Day Readmissions with Venous Thromboembolism Using Machine Learning” in the Journal of Nursing Scholarship as first author. Associate Professor Jung-Ah Lee is a co-author.
Hospitalization is one of the leading causes of VTE, a condition in which blood clots form. It can be fatal or disabling.
The tool was developed using machine learning and electronic health records to identify patients at high risk for VTE after they are released from the hospital. The study sample included 158,804 total admissions, of which 2,080 were VTE-positive.
“This tool could potentially guide treatment decisions for discharged patients and better outcomes,” Park says.