SCH: INT: Collaborative Research: Data-driven Stratification and Prognosis for Traumatic Brain Injury

创伤性脑损伤（TBI）是一个全球性的健康问题，影响着全世界1000多万人，是美国儿童和年轻人死亡和残疾的主要原因。虽然在过去的二十年中，对获得性脑损伤相关的生物学机制的理解有了显著的提高，但这些进展都没有转化为成功的临床试验，因此，在治疗这些关键疾病方面没有实质性的改进。创伤性脑损伤的异质性以及对危重患者进行可靠分层的能力是临床研究的主要挑战之一，这些危重患者可能通过某种干预措施获得更好的结果。为了应对这些挑战，该项目开发了一套全面的机器学习方法，可以广泛应用于各种问题。数据源既包括住院患者床边数据，也包括远程监控的远程医疗数据，从而将针对特定患者群体的多层次数据连接起来。这项研究对于支持危重病患者分层的试点计算模型的开发至关重要，并可能为降低这一患者群体的总体医疗保健和社会成本提供信息。该项目旨在开发新的计算算法，以可靠地对脑损伤患者进行分层，并根据多模式生理和临床数据预测其短期和长期预后。具体而言，本项目的研究目标是：(i)使用高效的子群模型，仅使用连贯相关变量的子集对患者进行分组，为任何给定患者开发可扩展且有效的个性化子群识别算法。还将建立判别子空间模型来区分患者的亚群。（ii）提出一种新的机器学习范式，称为“标签袋学习”，以识别和预测TBI患者的变化。标签袋学习的目标是学习数据中的一组标签及其相应的结果变量。该项目包括一个基于贝叶斯相关性的新框架，该框架可以自适应地转换任何现有的机器学习算法，并通过约束建模隐式地处理这个标签袋问题公式。（iii）通过差分子集建模框架开发一种新的长期结果预测方法。通过外展和教育活动，该项目将促进高中、本科生、研究生以及临床实习生的计算和系统思维。在这个项目中开发的方法将被整合到具有计算和生物医学重点的课程和教程中。该奖项反映了美国国家科学基金会的法定使命，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。

Traumatic Brain Injury (TBI) is a global health problem affecting over 10 million people worldwide and is a leading cause of death and disability among children and young adults in the United States. While the understanding of biological mechanisms related to acquired brain injuries has improved significantly in the past two decades, none of these advances have translated to a successful clinical trial and therefore, there has been no substantial improvement in treating such critical conditions. The heterogeneity of TBI and the ability to reliably stratify critically-ill patients who will likely have better outcomes for a certain intervention are amongst the major challenges in clinical research. To address these challenges, this project develops a comprehensive set of machine learning methods that can be broadly applied to a variety of problems. Data sources include both in-patient bedside data as well as remotely monitored telemedicine data, thus connecting data at multiple levels for specific patient populations. This research is crucial to support the development of pilot computational models for stratification of critical care patients and potentially inform ways to reduce the overall healthcare and societal costs for this patient population.The project aims to develop novel computational algorithms for reliably stratifying brain injury patients and predicting their short-term and long-term outcomes from multi-modal physiologic and clinical data. Specifically, the research objectives of this project are: (i) Develop a scalable and effective algorithm for personalized subgroup identification for any given patient using an efficient subcluster model that groups patients using only a subset of coherently relevant variables. Discriminative subspace models will also be built to distinguish subgroups of patients. (ii) Propose a new machine learning paradigm called 'Label-Bag learning' to identify and predict changes in TBI Patients. The goal of label-bag learning is to learn a group of labels and their corresponding outcome variable in the data. The project includes a new framework based on Bayesian correlations that can adaptively transform any existing machine learning algorithm and implicitly handle this label-bag problem formulation through constrained modeling. (iii) Develop a novel approach to long-term outcome prediction through differential subset modeling framework. Through outreach and educational activities, the project will promote computational and systems thinking among high school, undergraduate, and graduate students along with clinical trainees. Methods developed in this project will be integrated into courses and tutorials that have both computational and biomedical emphases.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.