Semantic interoperability for an AI-based applications platform for smart hospitals using HL7 FHIR

IF 3.7 2区计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Journal of Systems and Software Pub Date : 2024-05-16 DOI:10.1016/j.jss.2024.112093

Emmanouil S. Rigas , Paris Lagakis , Makis Karadimas , Evangelos Logaras , Dimitra Latsou , Magda Hatzikou , Athanasios Poulakidas , Antonis Billis , Panagiotis D. Bamidis

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引用次数: 0

Abstract

The digitization of the healthcare domain has the potential to drastically improve healthcare services, reduce the time to diagnosis, and lower costs. However, digital applications for the healthcare domain need to be interoperable to maximize their potential. Additionally, with the rapid expansion of Artificial Intelligence (AI) and, specifically, Machine Learning (ML), large amounts of diverse types of data are being utilized. Thus, to achieve interoperability in such applications, the adoption of common semantic data models becomes imperative. In this paper, we describe the adoption of such a common semantic data model, using the well-known Health Level Seven Fast Health Interoperability Resources (HL7 FHIR) standard, in a platform that assists in the creation and storage of a plethora of AI-based applications for several medical conditions. The FHIR server’s efficiency is being showcased by using it in an application predicting coronary artery stenosis as well as for managing the platform’s key performance indicators.

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利用 HL7 FHIR 实现基于人工智能的智慧医院应用平台的语义互操作性

医疗保健领域的数字化有可能极大地改善医疗保健服务，缩短诊断时间并降低成本。然而，医疗保健领域的数字应用需要具有互操作性，才能最大限度地发挥其潜力。此外，随着人工智能（AI），特别是机器学习（ML）的快速发展，大量不同类型的数据正在被利用。因此，要在此类应用中实现互操作性，采用通用语义数据模型势在必行。在本文中，我们介绍了在一个平台中采用这种通用语义数据模型的情况，该平台采用了众所周知的健康七级快速健康互操作性资源（HL7 FHIR）标准，可协助创建和存储大量基于人工智能的应用，适用于多种医疗条件。FHIR 服务器在预测冠状动脉狭窄以及管理平台关键性能指标的应用中的使用，展示了它的效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Systems and Software 工程技术-计算机：理论方法

CiteScore

8.60

自引率

5.70%

发文量

193

审稿时长

16 weeks

期刊介绍： The Journal of Systems and Software publishes papers covering all aspects of software engineering and related hardware-software-systems issues. All articles should include a validation of the idea presented, e.g. through case studies, experiments, or systematic comparisons with other approaches already in practice. Topics of interest include, but are not limited to: • Methods and tools for, and empirical studies on, software requirements, design, architecture, verification and validation, maintenance and evolution • Agile, model-driven, service-oriented, open source and global software development • Approaches for mobile, multiprocessing, real-time, distributed, cloud-based, dependable and virtualized systems • Human factors and management concerns of software development • Data management and big data issues of software systems • Metrics and evaluation, data mining of software development resources • Business and economic aspects of software development processes The journal welcomes state-of-the-art surveys and reports of practical experience for all of these topics.