Machine learning (ML) models are increasingly pivotal in automating clinical decisions. Yet, a glaring oversight in prior research has been the lack of proper processing of Electronic Medical Record (EMR) data in the clinical context for errors and outliers. Addressing this oversight, we introduce an innovative projections-based method that seamlessly integrates clinical expertise as domain constraints, generating important meta-data that can be used in ML workflows. In particular, by using high-dimensional mixed-integer programs that capture physiological and biological constraints on patient vitals and lab values, we can harness the power of mathematical "projections" for the EMR data to correct patient data. Consequently, we measure the distance of corrected data from the constraints defining a healthy range of patient data, resulting in a unique predictive metric we term as "trust-scores". These scores provide insight into the patient's health status and significantly boost the performance of ML classifiers in real-life clinical settings. We validate the impact of our framework in the context of early detection of sepsis using ML. We show an AUROC of 0.865 and a precision of 0.922, that surpasses conventional ML models without such projections.

机器学习模型在自动化临床决策中起着越来越重要的作用。我们介绍了一种基于投影的创新方法，将临床专业知识作为领域约束无缝融合，生成可用于机器学习工作流的重要元数据。通过捕捉生理和生物限制患者生命体征和实验室数值的高维混合整数规划，我们可以利用数学“投影”的力量纠正电子病历数据中的患者数据错误。我们测量修正后的数据与定义健康范围的约束之间的距离，得到一个称为“信任分数”的独特预测指标。这些分数提供了对患者健康状况的见解，并显著提升了机器学习分类器在现实临床环境中的性能。我们在早期脓毒症检测的背景下验证了我们框架的影响。我们展示了0.865的AUROC和0.922的精确度，超过了没有此类投影的常规机器学习模型。

自动数据校正的混合整数投影改进住院患者败血症预测