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，InSight 是一个机器学习- 基于脓毒症筛查系统，在目标临床站点的训练数据有限的情况下。 这项工作将使在较小或缺乏临床数据的地点进行前瞻性临床部署成为可能 存储库，通过将临床所需的训练数据量显着减少到几周 传统上，像 InSight 这样基于机器学习的系统需要对每个观察进行完整的再训练。 新的临床环境，进而需要从每个目标部署站点收集新的大量数据。 将通过转移学习技术来规避这一要求，该技术转移先前获得的知识 从源临床环境到新的目标环境的研究问题：哪些迁移学习方法和。 配对分类算法最适合与 InSight 一起使用，需要最少的目标站点训练数据 同时保持强大的性能？这些方法和算法在几种常见的情况下是否稳健？ 败血症谱定义？ 之前的工作：我们使用 MIMIC-III 回顾性数据开发了 InSight 集，脓毒症的受试者工作特征曲线下面积 (AUROC) 为 0.88 检测，4 小时早期脓毒症预测为 0.74 我们还进行了试点迁移学习。 ≥ 在不同的临床任务（死亡率预测）中进行的实验，其中迁移学习的效果提高了 10 倍 减少实现 AUROC 0.80 所需的目标站点训练数据量 具体目标：目标 1 - 并行实施和评估四种不同的迁移学习方法，用于回顾性临床脓毒症 预测任务，其中源数据集为MIMIC-III，模拟临床目标为抽取的数据集 来自 UCSF 的目标 2 - 确定目标 1 中哪些最佳方法也能提供稳健的性能。 当应用于另外两个脓毒症谱金标准时：我们将准备实施 使用实例迁移、残差学习和/或特征增强、内核的迁移学习方法 我们将使用适用的分类器在子集上测试这些方法。 UCSF 集，使用交叉验证并根据 AUROC 量化歧视性能。 方法/分类器对需要目标集中不超过 30 个脓毒症患者的示例并达到 相对于最佳测试，AUROC 在 0 小时和 4 小时脓毒症发作前预测/检测方面具有 0.05 的优势 替代筛选系统（目标 1）然后将测试前三对是否符合黄金标准。 选择，在这些测试中使用脓毒性休克（0 小时和 4 小时）和基于 SIRS 的脓毒症（0 小时）黄金标准； 至少一对必须再次在 AUROC 中与替代筛选系统相比达到 0.05 的优势幅度 （目标 2）：这些实验的结果将使 InSight 能够稳健地部署到 不同的临床部位，无需大量目标部位数据采集即可产生高性能。