I-Corps: Translation Potential of a Machine Learning Risk Stratification Tool for Venous Thromboembolism

Massachusetts Institute Of Technology, Cambridge MA

Investigators

Abstract

The broader impact of this I-Corps project is the development of a risk stratification software tool to predict a condition that occurs when a blood clot forms in a vein called venous thromboembolism (VTE). Venous thromboembolism ranks as the third leading cause of vascular-related deaths worldwide. Actions taken to prevent VTE have proven effective in reducing both morbidity and mortality. Despite being potentially preventable, diagnosing VTE remains a complex clinical challenge, largely attributable to the multitude of risk factors involved. This machine learning technology has the potential to manage and interpret the array of variables typically involved in predictive models and may influence VTE prevention strategies and clinical guidelines, promoting better patient outcomes. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on the development of a prediction algorithm using machine learning techniques designed to identify individuals at risk of venous thromboembolisms (VTE). The technology may be used to identify patterns and correlations that are not readily apparent through conventional statistical methods. This model may provide more accurate risk assessments for VTE. Through the analysis of an expansive dataset encompassing over 16,000 patient records, several key predictive risk factors were identified. These include patient age, the interval between trauma and treatment, and smoking status. Leveraging these insights, multiple predictive models were engineered and subjected to rigorous testing from other datasets. The most effective model demonstrated an ability to forecast VTE occurrences with an accuracy rate of 90%, showcasing its potential to significantly improve patient outcomes by facilitating early intervention and personalized care strategies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.