Wang Shaodong, Jiang Yiqun, Li Qing, Zhang Wenli
Department of Industrial & Manufacturing Systems Engineering, Iowa State University, Ames, IA, United States.
Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States.
JMIR AI. 2025 Aug 26;4:e72671. doi: 10.2196/72671.
Intensive care units (ICUs) treat patients with life-threatening illnesses. Worldwide, intensive care demand is massive. Predicting patient outcomes in ICUs holds significant importance for health care operation management. Nevertheless, it remains a challenging problem that researchers and health care practitioners have yet to overcome. While the newly emerging health digital trace data offer new possibilities, such data contain complex time series and patterns. Although researchers have devised severity score systems, traditional machine learning models with feature engineering, and deep learning models that use raw clinical data to predict ICU outcomes, existing methods have limitations.
This study aimed to develop a novel feature extraction and machine learning framework to repurpose and extract features with strong predictive power from patients' health digital traces for ICU outcome prediction.
Guided by signal processing techniques and medical domain knowledge, the proposed framework introduces a novel, signal processing-based feature engineering method to extract highly predictive features from ICU digital trace data. We rigorously evaluated this method on a real-world ICU dataset, demonstrating significant improvements over both traditional and deep learning baseline methods. The method was then evaluated using a real-world database to assess prediction accuracy and feature representativeness.
The prediction results obtained by the proposed framework significantly outperformed state-of-the-art benchmarks. This demonstrated the framework's effectiveness in capturing key patterns from complex health digital traces for improving ICU outcome prediction.
Our study contributes to health care operation management by leveraging digital traces from health care information systems to address challenges with significant implications for health care.
重症监护病房（ICU）治疗患有危及生命疾病的患者。在全球范围内，重症监护需求巨大。预测ICU患者的预后对于医疗运营管理具有重要意义。然而，这仍然是一个研究人员和医疗从业者尚未克服的挑战性问题。虽然新出现的健康数字轨迹数据提供了新的可能性，但此类数据包含复杂的时间序列和模式。尽管研究人员已经设计了严重程度评分系统、带有特征工程的传统机器学习模型以及使用原始临床数据来预测ICU预后的深度学习模型，但现有方法存在局限性。
本研究旨在开发一种新颖的特征提取和机器学习框架，以重新利用并从患者的健康数字轨迹中提取具有强大预测能力的特征，用于ICU预后预测。
在所提出的框架中，以信号处理技术和医学领域知识为指导，引入了一种新颖的、基于信号处理的特征工程方法，从ICU数字轨迹数据中提取具有高度预测性的特征。我们在一个真实世界的ICU数据集上对该方法进行了严格评估，结果表明其相对于传统和深度学习基线方法都有显著改进。然后使用一个真实世界数据库对该方法进行评估，以评估预测准确性和特征代表性。
所提出的框架获得的预测结果显著优于现有基准。这证明了该框架在从复杂的健康数字轨迹中捕捉关键模式以改善ICU预后预测方面的有效性。
我们的研究通过利用医疗信息系统中的数字轨迹，为医疗运营管理做出了贡献，以应对对医疗保健具有重大影响的挑战。
