III-CXT: Information Integration and Processing for Computer-Aided Trauma Decision Making

背景该资助的目标是：1）形成一个计算机辅助系统，以改善创伤性骨盆损伤的决策; 2）支持计算机科学中许多数据集成主题的研究。创伤是45岁以下美国人死亡的主要原因。创伤性骨盆损伤可能由于严重出血而致命，治疗这些损伤的护理人员需要考虑几种类型的数据，包括生物医学信号，图像，创伤评分，实验室结果，诊断/治疗，损伤细节和人口统计学在决策过程中。整合简单类型的数据（如实验室结果和人口统计数据）并不容易，但当试图整合更复杂类型的患者数据（如生物医学信号和图像）时，决策过程显示出其真正的复杂性。该项目在以下方面具有挑战性：o它构建了一个创伤性骨盆损伤数据库，其中包括每个患者的所有相关生物医学信号/图像，创伤评分，实验室结果，诊断，治疗，人口统计学和损伤细节。与现有数据库相比，该数据库将具有两个显著优势：1）它不仅包含患者人口统计数据和创伤评分，还包含生理测量和图像的时间序列（信号）; 2）在新数据库中，患者信息经过处理并转换为可直接用于决策的一组特征，而不是仅包含原始数据。o将形成各种新颖的生物医学信号和图像处理方法来提取相关特征。这些计算方法将包括信号和图像处理中的计算方法的改进版本（例如，用于CT图像的分割技术），以及用于特定信号和图像的特征提取方法（例如，定义从CT捕获的骨盆环的总面积作为特征）。该项目构建了一个规则数据库，其中使用非线性分类和回归树分析了特征数据库中患者的所有衍生特征以及结果，从而产生一组规则来描述输入特征之间的逻辑关系以及结果/建议。该项目在规则验证方面是新颖的;除了使用典型的统计方法如交叉验证和现有系统中使用的灵敏度和特异性的测量之外，一个基于计算学习理论的新的统计框架将被用来对新系统与其他方法（如神经网络和贝叶斯分类器）进行更全面的比较。更广泛的影响这个项目将计算机科学家和创伤专家聚集在一起并将产生一个可以在其他医院系统复制的系统。这种方法可以用于其他类型的创伤病例，如脑损伤。在教育方面，项目成果将纳入定期研讨会，向创伤护理领域的医疗保健提供者传授创伤护理中使用的最新技术。PI将使研究项目与北卡罗来纳州夏洛特大学IT多样性研究所管理的本科生和研究生研究和外联活动保持一致，该研究所的使命是增加IT领域女性和代表性不足群体的入学率和保留率，重点是促进研究生和本科生跨学科课程，以及研究所内的两个NSF资助的课程：1）学生&技术在学术界，研究和服务联盟：东南伙伴关系，扩大参与计算，和2）计算研究的本科生。

ContextThe goal of this grant is to 1) form a computer-aided system to improve decision-making for traumatic pelvic injuries and 2) support research on a number of data integration topics in computer science. Trauma is the leading cause of death for Americans under the age of 45. Traumatic pelvic injuries can be fatal due to severe hemorrhage, and care givers treating these injuries need to consider several types of data, including biomedical signals, images, trauma scores, laboratory results, diagnosis/treatment, injury specifics, and demographics during the decision making process. Integrating simple types of data such as lab results and demographics is not easy, but the decision-making process shows its true complexity when trying to integrate more complex types of patient data such as biomedical signals and images. Intellectual MeritThe project is challenging in the following aspects: o It constructs a traumatic pelvic injury database that includes all relevant biomedical signals/images, trauma scores, lab results, diagnosis, treatment, demographics, and injury specifics for each patient. This database will have two significant advantages over existing databases: 1) it will contain not only patient demographics and trauma scores but also time-series (signals) of physiological measures and images; and 2) in the new database, instead of including only raw data, patient information is processed and transformed into a set of features that can be directly used for decision making. o A variety of novel biomedical signal and image processing methods will be formed to extract relevant features. These computational methods will include both the improved versions of computational methods in signal and image processing (e.g., segmentation techniques for CT images), and feature extraction methods for specific signals and images (e.g., defining the total area of the pelvic ring captured from CT as a feature). o The project constructs a rule database where all derived features for patients in the feature database are analyzed with outcomes, resulting in a set of rules to describe logical relationships among the input features and resulting outcomes/recommendations, using non-linear classification and regression tree. The project is novel in its rule validation; besides using typical statistical methods such as cross-validation and measures of sensitivity and specificity used in existing systems, a new statistical framework based on computational learning theory will be used to allow a more comprehensive comparison of the new system with other methods such as neural networks and Bayesian classifiers.Broader ImpactsThis project brings together computer scientists with trauma experts and will produce a system that can be replicated at other hospital systems. This methodology can be used for other types of trauma cases, such as brain injuries. Educationally, project results will be included in regular seminars to teach healthcare providers across the spectrum of the trauma care the latest techniques used in trauma care. The PI will align the research project with undergraduate and graduate research and outreach activities managed by the University of North Carolina at Charlotte''s Diversity in IT Institute, whose mission is to increase enrollment and retention of women and underrepresented groups within IT with a focus on facilitating graduate and undergraduate interdisciplinary programs, and two NSF-funded programs housed within the institute: 1) The Students & Technology in Academia, Research, and Service Alliance: A Southeastern Partnership for Broadening Participation in Computing, and 2) Computing Research for Undergraduates.