Department of Information Management, National Central University, Taoyuan, Taiwan.
College of Intelligent Computing, Chang Gung University, Taoyuan, Taiwan; Medical Statistics Research Center, Chang Gung University, Taoyuan, Taiwan; Department of Emergency Medicine, Chang Gung Memorial Hospital, Keelung, Taiwan.
Comput Methods Programs Biomed. 2024 Oct;255:108336. doi: 10.1016/j.cmpb.2024.108336. Epub 2024 Jul 20.
Machine learning models are vital for enhancing healthcare services. However, integrating them into health information systems (HISs) introduces challenges beyond clinical decision making, such as interoperability and diverse electronic health records (EHR) formats. We proposed Model Cabinet Architecture (MoCab), a framework designed to leverage fast healthcare interoperability resources (FHIR) as the standard for data storage and retrieval when deploying machine learning models across various HISs, addressing the challenges highlighted by platforms such as EPOCH®, ePRISM®, KETOS, and others.
The MoCab architecture is designed to streamline predictive modeling in healthcare through a structured framework incorporating several specialized parts. The Data Service Center manages patient data retrieval from FHIR servers. These data are then processed by the Knowledge Model Center, where they are formatted and fed into predictive models. The Model Retraining Center is crucial in continuously updating these models to maintain accuracy in dynamic clinical environments. The framework further incorporates Clinical Decision Support (CDS) Hooks for issuing clinical alerts. It uses Substitutable Medical Apps Reusable Technologies (SMART) on FHIR to develop applications for displaying alerts, prediction results, and patient records.
The MoCab framework was demonstrated using three types of predictive models: a scoring model (qCSI), a machine learning model (NSTI), and a deep learning model (SPC), applied to synthetic data that mimic a major EHR system. The implementations showed how MoCab integrates predictive models with health data for clinical decision support, utilizing CDS Hooks and SMART on FHIR for seamless HIS integration. The demonstration confirmed the practical utility of MoCab in supporting clinical decision making, validated by its application in various healthcare settings.
We demonstrate MoCab's potential in promoting the interoperability of machine learning models and enhancing its utility across various EHRs. Despite facing challenges like FHIR adoption, MoCab addresses key challenges in adapting machine learning models within healthcare settings, paving the way for further enhancements and broader adoption.
机器学习模型对于提升医疗保健服务至关重要。然而，将其整合到健康信息系统（HIS）中不仅涉及临床决策，还包括互操作性和多样化的电子健康记录（EHR）格式等挑战。我们提出了模型柜架构（MoCab），这是一个框架，旨在利用快速医疗保健互操作性资源（FHIR）作为标准，在各种 HIS 中部署机器学习模型时进行数据存储和检索，解决了 EPOCH®、ePRISM®、KETOS 等平台所强调的挑战。
MoCab 架构旨在通过一个结构化框架，通过几个专门的部分来简化医疗保健中的预测建模。数据服务中心管理从 FHIR 服务器检索患者数据。然后，这些数据由知识模型中心处理，在那里进行格式化并输入到预测模型中。模型再训练中心对于在动态临床环境中保持准确性至关重要，它用于不断更新这些模型。该框架还包含用于发出临床警报的临床决策支持（CDS）挂钩。它使用 FHIR 上的可替换医疗应用程序可重用技术（SMART）开发用于显示警报、预测结果和患者记录的应用程序。
使用三种类型的预测模型（评分模型（qCSI）、机器学习模型（NSTI）和深度学习模型（SPC））演示了 MoCab 框架，这些模型应用于模拟主要 EHR 系统的合成数据。实施情况表明 MoCab 如何将预测模型与健康数据集成以支持临床决策，利用 CDS 挂钩和 FHIR 上的 SMART 实现 HIS 的无缝集成。演示证实了 MoCab 在支持临床决策方面的实际效用，其在各种医疗保健环境中的应用验证了这一点。
我们展示了 MoCab 在促进机器学习模型的互操作性和增强其在各种 EHR 中的实用性方面的潜力。尽管面临 FHIR 采用等挑战，但 MoCab 解决了在医疗保健环境中适应机器学习模型的关键挑战，为进一步增强和更广泛的采用铺平了道路。
