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

I-Corps：机器学习风险分层工具对静脉血栓栓塞的转化潜力

• 批准号: 2420417
• 负责人: Roman Lubynsky
• 金额: $ 5万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2420417&HistoricalAwards=false
• 关键词: Corps; Translation; Potential; Machine; Learning

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.

该 I-Corps 项目更广泛的影响是开发了一种风险分层软件工具，用于预测静脉中形成血栓时出现的称为静脉血栓栓塞 (VTE) 的病症。静脉血栓栓塞是全球血管相关死亡的第三大原因。 事实证明，预防 VTE 的行动可有效降低发病率和死亡率。尽管有可能预防，但诊断 VTE 仍然是一项复杂的临床挑战，很大程度上归因于涉及多种风险因素。这种机器学习技术有潜力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 VTE 预防策略和临床指南，从而促进更好的患者治疗结果。该 I-Corps 项目利用体验式学习与第一手调查相结合行业生态系统来评估技术的转化潜力。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传统统计方法不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。通过对包含 16,000 多份患者记录的广泛数据集进行分析，确定了几个关键的预测风险因素。这些包括患者年龄、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。最有效的模型展示了预测 VTE 发生的能力，准确率高达 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，并被认为值得通过以下方式获得支持：使用基金会的智力价值和更广泛的影响审查标准进行评估。