School of Mechanical, Industrial and Manufacturing Engineering at Oregon State University, Corvallis, OR 97331, USA.
Edward P. Fitts Department of Industrial and Systems Engineering at North Carolina State University, Raleigh, NC 27695, USA.
Artif Intell Med. 2022 Oct;132:102406. doi: 10.1016/j.artmed.2022.102406. Epub 2022 Sep 21.
Sepsis is the body's adverse response to infection which can lead to septic shock and eventually death if not treated in a timely manner. Analyzing patterns in sepsis patients' health status over time can help predict septic shock before its onset allowing healthcare providers to be more proactive. Temporal pattern mining methods can be used to identify trends in a patient's health status over time. If these methods return too many patterns, however, this can hinder knowledge discovery and practical implementation at the bedside in acute care settings. We propose a framework to find a small number of relevant temporal patterns in electronic health records for the early prediction of septic shock. Our framework consists of a temporal pattern mining method and three pattern selection techniques based on non-contrasted group support (PST1), contrasted group support (PST2), and model predictive power (PST3, PST4). We find that model-based feature selection approaches PST3 and PST4 yield the best prediction performance among these techniques. However, PST2 identifies more multi-state patterns with abnormal health states, which can give healthcare providers indicators of patient deterioration towards septic shock. Hence, from a knowledge discovery perspective, it may be worthwhile to sacrifice a small amount of prediction power for actionable patient health information through the implementation of PST2.
败血症是人体对感染的不良反应，如果不及时治疗，可能会导致感染性休克，最终导致死亡。分析败血症患者健康状况随时间的变化模式有助于在其发作前预测感染性休克，从而使医疗保健提供者能够更积极主动地进行治疗。时间模式挖掘方法可用于识别患者健康状况随时间的变化趋势。然而，如果这些方法返回的模式过多，可能会阻碍在急性护理环境中的床边进行知识发现和实际实施。我们提出了一个框架，用于在电子健康记录中找到少量相关的时间模式，以实现对败血症性休克的早期预测。我们的框架包括一种时间模式挖掘方法和三种基于非对比组支持（PST1）、对比组支持（PST2）和模型预测能力（PST3、PST4）的模式选择技术。我们发现基于模型的特征选择方法 PST3 和 PST4 在这些技术中具有最佳的预测性能。然而，PST2 确定了更多具有异常健康状态的多状态模式，这可以为医疗保健提供者提供患者向败血症性休克恶化的指标。因此，从知识发现的角度来看，通过实施 PST2 为可操作的患者健康信息牺牲少量预测能力可能是值得的。
