A computational approach to early sepsis detection

摘要 意义：在这个SBIR项目中，我们建议改进机器学习--Insight的性能-- 基于脓毒症筛查系统，在来自目标临床站点的训练数据有限的情况下。建议数 这项工作将使预期的临床部署成为可能，部署到较小或缺乏临床数据的地点 存储库，通过将必要的培训数据量显著减少到几周的临床 观察。传统上，像Insight这样的基于机器学习的系统需要对每个人进行完全的再培训 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。我们 将通过转移学习技术来绕过这一要求，该技术转移先前获得的知识 在源临床环境中转换为新的目标环境。研究问题：哪些迁移学习方法和 配对分类算法最适合与洞察力一起使用，只需要最少的目标站点训练数据 同时保持强劲的业绩？这些方法和算法在几个常见的 败血症-谱系定义？之前的工作：我们已经使用MIMIC-III回顾数据开发了洞察力 SET，在该SET上，脓毒症的接受者操作特征曲线(AUROC)下的面积为0.88 检测，0.74用于4小时早期脓毒症预测。我们还进行了试点迁移学习 ≥ 在不同的临床任务，死亡率预测的实验中，迁移学习产生了10倍的 减少达到AUROC 0.80所需的目标现场培训数据量。具体目标：目标1-- 实施和评估四种不同的迁移学习方法对一例回顾性临床脓毒症的治疗 预测任务，其中源数据集是MIMIC-III，模拟的临床目标是绘制的数据集 加州大学旧金山分校的。目标2-确定目标1中最好的方法中哪一种也能提供稳健的性能 当应用于另外两个败血症光谱黄金标准时。方法：我们将准备 使用实例转移、残差学习和/或特征增强的转移学习方法，核 长度比例传递和特征传递。我们将使用适用的分类器在以下子集上测试这些方法 UCSF集合，使用交叉验证，并根据AUROC量化区分性能。最好的 方法/分类器对将需要目标集合中不超过30个脓毒症患者的实例并达到 相对于最好的测试，AUROC在0小时和4小时发病前脓毒症预测/检测方面的优势为0.05 替代筛查系统(目标1)。然后将测试前三对对黄金标准的稳健性 选择，使用感染性休克(0和4小时)和基于全身炎症反应综合征(0小时)的脓毒症黄金标准；在这些测试中， 在AUROC中，至少有一对必须再次达到相对于替代筛选系统的0.05优势边际 (目标2)。未来方向：这些实验的结果将使洞察力有力地应用于 多样化的临床站点，无需广泛的目标站点数据采集即可产生高性能。

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.