Amrollahi Fatemeh, Shashikumar Supreeth Prajwal, Kathiravelu Pradeeban, Sharma Ashish, Nemati Shamim
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5610-5614. doi: 10.1109/EMBC44109.2020.9175947.
Sepsis, a dysregulated immune response to infection, has been the leading cause of morbidity and mortality in critically ill patients. Multiple studies have demonstrated improved survival outcomes when early treatment is initiated for septic patients. In our previous work, we developed a real-time machine learning algorithm capable of predicting onset of sepsis four to six hours prior to clinical recognition. In this work, we develop AIDEx, an open-source platform that consumes data as FHIR resources. It is capable of consuming live patient data, securely transporting it into a cloud environment, and monitoring patients in real-time. We build AIDEx as an EHR vendor-agnostic open-source platform that can be easily deployed in clinical environments. Finally, the computation of the sepsis risk scores uses a common design pattern that is seen in streaming clinical informatics and predictive analytics applications. AIDEx provides a comprehensive case study in the design and development of a production-ready ML platform that integrates with Healthcare IT systems.
脓毒症是一种对感染的免疫反应失调，一直是重症患者发病和死亡的主要原因。多项研究表明，对脓毒症患者尽早进行治疗可改善生存结果。在我们之前的工作中，我们开发了一种实时机器学习算法，能够在临床识别前四至六小时预测脓毒症的发作。在这项工作中，我们开发了AIDEx，这是一个以FHIR资源形式消费数据的开源平台。它能够消费实时患者数据，将其安全传输到云环境中，并实时监测患者。我们将AIDEx构建为一个与电子健康记录（EHR）供应商无关的开源平台，可轻松部署在临床环境中。最后，脓毒症风险评分的计算使用了一种在流式临床信息学和预测分析应用中常见的设计模式。AIDEx提供了一个全面的案例研究，涉及一个可投入生产的ML平台的设计和开发，该平台与医疗保健信息技术系统集成。
