Building and implementing a TBI prognostic model featuring real-time analysis of brain CT images

工作范围 杜克大学将完成所有工作，为机器学习模型的构建和模型的实施注入 杜克大学临床工作流程。对于项目的目标1，这项工作将包括数据提取和清理、神经网络 架构设计，以及模型优化和验证。对于目标2，这项工作将包括建立 用于实时图像以及访问和处理的技术基础设施，在 与一线临床医生密切合作，并部署和预期验证该模型。后者 STEP还将包括对医院用户的教育和培训。对于该项目的目标3，杜克大学将指导员工 杰斐逊通过模型的实施和验证过程，配合积极的整合和培训 由杰斐逊的工作人员表演。在《目标3》中，杜克还将进行多站点模型泛化实验， 使用杜克大学和杰斐逊大学的回溯数据。数据将通过Secure在杜克和杰斐逊之间共享 杰斐逊的安全数据仓库和杜克的受保护的分析和计算之间的以太网传输 环境该工作的最终目标将是提供一种复杂、高精度和无缝的 预测脑外伤患者住院期间可操作的脑损伤并发症风险的综合工具 相遇。这种方法将增强治疗复杂神经疾病的决策能力，将 显著改善TBI的整体转归，降低再住院率，并最大限度地减少额外费用 由于医疗保健资源的提供效率低下而引起的。

Scope of Work Duke will complete all work for the machine learning model building and implementation of the model into the Duke clinical workflow. For Aim 1 of the project, this work will include data extraction and cleaning, neural network architecture design, and model optimization and validation. For Aim 2, this work will include establishment of technical infrastructure for real-time image and access and processing, construction of a front-end dashboard in close collaboration with frontline clinicians, and deployment and prospective validation of the model. The latter step will also consist of education and training of hospital users. For Aim 3 of the project, Duke will guide staff at Jefferson through the model implementation and validation process, with the active integration and training performed by staff at Jefferson. In Aim 3 Duke will also run the experiments on multi-site model generalization, using retrospective data at both Duke and Jefferson. Data will be shared between Duke and Jefferson via secure ethernet transfer between Jefferson’s secure data warehouse and Duke’s Protected Analytics and Computing Environment. The end goal of the work will be to provide a sophisticated, high-accuracy, and seamlessly integrated tool for predicting the risk of actionable TBI complications over the course of a TBI patient’s hospital encounter. This method, which will augment decision-making for treating a complex neurological condition, will significantly improve overall TBI outcomes, reduce readmission rates, and minimize the extraordinary costs incurred by inefficient provision of healthcare resources.