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

这个I-Corps项目的更广泛的影响是开发了一种风险分层软件工具，用于预测静脉血栓栓塞（VTE）中形成血块时发生的情况。静脉血栓栓塞是全球血管相关死亡的第三大原因。 为预防静脉血栓栓塞而采取的行动已证明在降低发病率和死亡率方面是有效的。尽管有可能预防，但诊断VTE仍然是一个复杂的临床挑战，主要归因于涉及的众多风险因素。这种机器学习技术有可能管理和解释预测模型中通常涉及的一系列变量，并可能影响静脉血栓栓塞预防策略和临床指南，促进更好的患者结局。这个I-Corps项目利用经验学习结合对行业生态系统的第一手调查来评估该技术的转化潜力。该解决方案基于使用机器学习技术开发的预测算法，旨在识别有静脉血栓栓塞（VTE）风险的个体。该技术可用于识别通过常规统计方法不容易显现的模式和相关性。该模型可以为VTE提供更准确的风险评估。通过分析包含超过16，000份患者记录的广泛数据集，确定了几个关键的预测风险因素。这些因素包括患者年龄、创伤和治疗之间的间隔以及吸烟状况。利用这些见解，设计了多个预测模型，并接受了其他数据集的严格测试。最有效的模型证明了预测VTE发生的能力，准确率为90%，展示了其通过促进早期干预和个性化护理策略显着改善患者预后的潜力。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.