Pathiraja Rathnayaka Hitige Nadeesha, Song Ting, Craig Steven J, Davis Kimberley J, Hao Xubing, Cui Licong, Yu Ping
Department of Information and Communication Technology, Faculty of Technology, Rajarata University of Sri Lanka, Mihintale 50300, Sri Lanka.
Centre for Digital Transformation, School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2522, Australia.
Healthcare (Basel). 2024 Dec 24;13(1):10. doi: 10.3390/healthcare13010010.
Traditional methods for analysing surgical processes often fall short in capturing the intricate interconnectedness between clinical procedures, their execution sequences, and associated resources such as hospital infrastructure, staff, and protocols.
This study addresses this gap by developing an ontology for appendicectomy, a computational model that comprehensively represents appendicectomy processes and their resource dependencies to support informed decision making and optimise appendicectomy healthcare delivery.
The ontology was developed using the NeON methodology, drawing knowledge from existing ontologies, scholarly literature, and de-identified patient data from local hospitals.
The resulting ontology comprises 108 classes, including 11 top-level classes and 96 subclasses organised across five hierarchical levels. The 11 top-level classes include "clinical procedure", "appendicectomy-related organisational protocols", "disease", "start time", "end time", "duration", "appendicectomy outcomes", "hospital infrastructure", "hospital staff", "patient", and "patient demographics". Additionally, the ontology includes 77 object and data properties to define relationships and attributes. The ontology offers a semantic, computable framework for encoding appendicectomy-specific clinical procedures and their associated resources.
By systematically representing this knowledge, this study establishes a foundation for enhancing clinical decision making, improving data integration, and ultimately advancing patient care. Future research can leverage this ontology to optimise healthcare workflows and outcomes in appendicectomy management.
分析手术过程的传统方法在捕捉临床程序、执行顺序以及医院基础设施、人员和协议等相关资源之间复杂的相互联系方面往往存在不足。
本研究通过开发阑尾切除术本体来填补这一空白，这是一种计算模型，全面表示阑尾切除手术过程及其资源依赖关系，以支持明智的决策并优化阑尾切除手术的医疗服务。
该本体采用NeON方法开发，从现有本体、学术文献以及当地医院的去识别患者数据中获取知识。
生成的本体包含108个类，包括11个顶级类和96个子类，分为五个层次级别。11个顶级类包括“临床程序”、“阑尾切除相关组织协议”、“疾病”、“开始时间”、“结束时间”、“持续时间”、“阑尾切除结果”、“医院基础设施”、“医院工作人员”、“患者”和“患者人口统计学”。此外，本体还包括77个对象和数据属性来定义关系和属性。该本体为编码特定于阑尾切除术的临床程序及其相关资源提供了一个语义可计算框架。
通过系统地表示这些知识，本研究为加强临床决策、改善数据整合以及最终推进患者护理奠定了基础。未来的研究可以利用这个本体来优化阑尾切除手术管理中的医疗工作流程和结果。
