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

SCH：INT：协作研究：数据驱动的脑外伤分层和预后

• 批准号: 1838745
• 负责人: Vignesh Subbian
• 金额: $ 50.36万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1838745&HistoricalAwards=false
• 关键词: SCH; INT; Collaborative; Research; Data

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.

创伤性脑损伤（TBI）是一个全球性的健康问题，影响全球超过1000万人，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，对与获得性脑损伤相关的生物学机制的理解有了显着的改善，但这些进展都没有转化为成功的临床试验，因此，在治疗此类危重疾病方面没有实质性的改善。TBI的异质性和可靠地对危重患者进行分层的能力是临床研究的主要挑战之一，这些患者可能会对某种干预措施产生更好的结果。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，可广泛应用于各种问题。数据源包括住院患者床边数据以及远程监测的远程医疗数据，从而将特定患者群体的多个层面的数据连接起来。这项研究是至关重要的，以支持试点计算模型的发展，重症监护患者的分层和潜在的通知方式，以减少整体医疗保健和社会成本为这一患者population.The项目的目的是开发新的计算算法，可靠地分层脑损伤患者和预测他们的短期和长期的结果，从多模态的生理和临床数据。具体而言，该项目的研究目标是：（i）开发一种可扩展的和有效的算法，用于任何给定患者的个性化亚组识别，使用一种有效的子聚类模型，仅使用相干相关变量的子集对患者进行分组。还将建立判别子空间模型以区分患者亚组。(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对应的结果变量。该项目包括一个基于贝叶斯相关性的新框架，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。(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。通过推广和教育活动，该项目将促进高中，本科生和研究生沿着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课程和教程，有计算和生物医学的重点。这个奖项反映了NSF的法定使命，并已被认为是值得通过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的支持。

项目成果

Subspace Clustering of Physiological Data From Acute Traumatic Brain Injury Patients: Retrospective Analysis Based on the PROTECT III Trial

急性创伤性脑损伤患者生理数据的子空间聚类：基于 PROTECT III 试验的回顾性分析

• DOI: 10.2196/24698
• 发表时间: 2021
• 期刊: JMIR Biomedical Engineering
• 作者: Ehsani, Sina;Reddy, Chandan K;Foreman, Brandon;Ratcliff, Jonathan;Subbian, Vignesh
• 通讯作者: Subbian, Vignesh

Predicting Failure of Noninvasive Respiratory Support Using Deep Recurrent Learning

使用深度循环学习预测无创呼吸支持的失败

• DOI: 10.4187/respcare.10382
• 发表时间: 2023
• 期刊: Respiratory Care
• 影响因子: 2.5
• 作者: Essay, Patrick T;Mosier, Jarrod M;Nayebi, Amin;Fisher, Julia M;Subbian, Vignesh
• 通讯作者: Subbian, Vignesh

A self-supervised learning-based approach to clustering multivariate time-series data with missing values (SLAC-Time): An application to TBI phenotyping

• DOI: 10.1016/j.jbi.2023.104401
• 发表时间: 2023-02
• 期刊: Journal of biomedical informatics
• 影响因子: 4.5
• 作者: Hamid Ghaderi;B. Foreman;Amin Nayebi;Sindhu Tipirneni;Chandan K. Reddy;V. Subbian

An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain Injury

• DOI: 10.48550/arxiv.2208.06717
• 发表时间: 2022-08
• 期刊: AMIA ... Annual Symposium proceedings. AMIA Symposium
• 作者: Amin Nayebi;Sindhu Tipirneni;B. Foreman;Chandan K. Reddy;V. Subbian

WindowSHAP: An efficient framework for explaining time-series classifiers based on Shapley values

WindowSHAP：基于 Shapley 值解释时间序列分类器的有效框架

• DOI: 10.1016/j.jbi.2023.104438
• 发表时间: 2023
• 期刊: Journal of Biomedical Informatics
• 影响因子: 4.5
• 作者: Nayebi, Amin;Tipirneni, Sindhu;Reddy, Chandan K.;Foreman, Brandon;Subbian, Vignesh
• 通讯作者: Subbian, Vignesh