A computational approach to early sepsis detection

早期脓毒症检测的计算方法

• 批准号: 9557664
• 负责人: Ritankar Das
• 金额: $ 31.08万
• 依托单位: DASCENA, INC.
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9557664
• 关键词: Address; Age; Algorithms; Area; Cessation of life; Classification; Clinical; Clinical Decision Support Systems; Collection; Custom; Data; Data Collection; Data Set; Detection; Discrimination; Drops; Early Diagnosis; Early Intervention; Future; Gold; Healthcare; Healthcare Systems; Hour; Image; Immune response; Institution; Knowledge; Learning; Length; Machine Learning; Medical; Methods; Multicenter Studies; Nature; Patient-Focused Outcomes; Patients; Performance; Psychological Transfer; Receiver Operating Characteristics; Research; Residual state; Risk; SCAP2 gene; Sensitivity and Specificity; Sepsis; Septic Shock; Severities; Side; Site; Small Business Innovation Research Grant; Source; Survival Rate; System; Techniques; Testing; Training; Validation; Work; base; clinical data warehouse; clinical decision support; clinical research site; cost; data acquisition; experimental study; improved; insight; learning strategy; mortality; performance site; portability; prospective; screening; septic; septic patients; success; support tools

Abstract Significance: In this SBIR project, we propose to improve the performance of InSight, a machine-learning- based sepsis screening system, in situations of limited training data from the target clinical site. The proposed work will make possible prospective clinical deployments to sites which are smaller or lack clinical data repositories, by significantly reducing the amount of training data necessary down to a few weeks of clinical observation. Classically, a machine-learning-based system like InSight requires complete retraining for each new clinical setting, in turn requiring a new and large collection of data from each target deployment site. We will circumvent this requirement via transfer learning techniques, which transfer knowledge acquired previously in a source clinical setting to a new, target setting. Research Questions: Which transfer learning methods and paired classification algorithms are most suitable for use with InSight, requiring minimal target-site training data while maintaining strong performance? Are these methods and algorithms robust across the several common sepsis-spectrum definitions? Prior Work: We have developed InSight using the MIMIC-III retrospective data set, on which it attains an area under the receiver operating characteristic curve (AUROC) of 0.88 for sepsis detection, and 0.74 for 4-hour early sepsis prediction. We have also conducted pilot transfer learning ≥ experiments in a different clinical task, mortality forecasting, in which transfer learning yields a 10-fold reduction in the amount of target-site training data required to achieve AUROC 0.80. Specific Aims: Aim 1 - to implement and assess side-by-side four diverse transfer learning methods for a retrospective clinical sepsis prediction task, where the source data set is MIMIC-III and the simulated clinical target is a data set drawn from UCSF. Aim 2 - to determine which among the best methods from Aim 1 also provide robust performance when applied to two additional sepsis-spectrum gold standards. Methods: We will prepare implementations of transfer learning methods which use instance transfer, residual learning and/or feature augmentation, kernel length scale transfer, and feature transfer. We will test these methods with applicable classifiers on subsets of the UCSF set, using cross-validation and quantifying discrimination performance in terms of AUROC. The best method/classifier pairs will require no more than 30 examples of septic patients from the target set and attain AUROC superiorities of 0.05 in 0- and 4-hour pre-onset sepsis prediction/detection, relative to the best tested alternative screening systems (Aim 1). The top three pairs will then be tested for robustness to gold standard choice, using septic shock (0- and 4-hour) and SIRS-based sepsis (0-hour) gold standards; in these tests, at least one pair must again attain 0.05 margin of superiority in AUROC versus the alternative screening systems (Aim 2). Future Directions: The results of these experiments will enable InSight to be robustly deployed to diverse clinical sites, yielding high performance without the need for extensive target-site data acquisition.

抽象的 意义：在这个SBIR项目中，我们建议提高Insight的性能，这是一种机器学习 - 在目标临床部位的有限培训数据的情况下，基于败血症筛查系统。提议 工作将使可能的前瞻性临床部署到较小或缺乏临床数据的网站 存储库，通过将必要的培训数据量显着减少到几周的临床 观察。从经典上讲，基于机器学习的系统（例如Insight）需要为每个系统进行完整的重新培训 新的临床环境，反过来需要从每个目标部署站点收集新的大量数据。我们 将通过转移学习技术来规避这一要求，这些技术先前获取了转移知识 在新的目标设置的来源临床环境中。研究问题：哪种转移学习方法和 配对分类算法最适合与洞察力一起使用，需要最小的目标位点训练数据 在保持良好的表现的同时？这些方法和算法是否在几个常见 败血症谱的定义？先前的工作：我们已经使用MIMIC-III回顾数据开发了洞察力 集合，它在接收器操作特征曲线（AUROC）下达到败血症的区域为0.88 检测，4小时早期预测为0.74。我们还进行了飞行员转移学习 ≥ 在不同的临床任务，死亡率预测中进行的实验，其中转移学习产生了10倍 减少实现AUROC 0.80所需的目标位点训练数据的数量。具体目的：目标1- 进行回顾性脓毒症的并排实施和评估四种不同的转移学习方法 预测任务，其中源数据集为模拟III，模拟临床目标是数据集绘制 来自UCSF。 AIM 2-确定AIM 1的最佳方法中的哪种也提供了强大的性能 当应用于另外两个败血症的金标准。方法：我们将准备 使用实例转移，剩余学习和/或功能增强的转移学习方法，内核 长度尺度传输和特征传输。我们将使用适用的分类器测试这些方法 使用交叉验证并根据AUROC来量化歧视性能。最好的 方法/分类器对将不超过30个目标患者的示例，并达到目标。 相对于最佳测试的，在0和4小时的败血症预测/检测中为0.05的AUROC优势为0.05 替代筛查系统（AIM 1）。然后，前三对将进行测试，以达到黄金标准的鲁棒性 选择，使用败血性休克（0和4小时）和基于SIRS的败血症（0小时）金标准；在这些测试中，在 至少一对必须再次在AUROC中获得0.05优势与替代筛选系统的优势 （目标2）。未来的方向：这些实验的结果将使洞察力能够牢固地部署到 潜水员临床部位，产生高性能，而无需广泛的目标位点数据采集。