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An Ontology-Based Approach for Understanding Appendicectomy Processes and Associated Resources.

Nadeesha Pathiraja Rathnayaka Hitige1,2, Ting Song2, Steven J Craig3,4

  • 1Department of Information and Communication Technology, Faculty of Technology, Rajarata University of Sri Lanka, Mihintale 50300, Sri Lanka.

Healthcare (Basel, Switzerland)
|January 10, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an ontology for appendicectomy, a computational model detailing surgical processes and resource links. This enhances clinical decision-making and optimizes appendicectomy healthcare delivery.

Keywords:
appendicectomyontologyprocess miningresourcessurgical process

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Area of Science:

  • Medical Informatics
  • Ontology Engineering
  • Health Services Research

Background:

  • Traditional analysis of surgical processes lacks comprehensive understanding of resource interdependencies.
  • Existing methods fail to capture the intricate links between clinical procedures, execution sequences, and hospital resources.

Purpose of the Study:

  • Develop a computational ontology for appendicectomy (surgical removal of the appendix).
  • To create a model representing appendicectomy processes and their resource dependencies.
  • To support informed decision-making and optimize appendicectomy healthcare delivery.

Main Methods:

  • Ontology development utilizing the NeON methodology.
  • Knowledge acquisition from existing ontologies, scholarly literature, and de-identified hospital patient data.

Main Results:

  • A comprehensive ontology with 108 classes (11 top-level) and 77 properties.
  • The ontology semantically represents appendicectomy procedures and associated resources.
  • Includes classes for procedures, protocols, disease, time, outcomes, infrastructure, staff, and patient demographics.

Conclusions:

  • The ontology provides a computable framework for appendicectomy knowledge.
  • Establishes a foundation for improved clinical decision-making and data integration.
  • Offers potential for optimizing appendicectomy healthcare workflows and patient outcomes.