Rendering knowledge graphs from aerospace dentistry processes for clinical decision support systems

Area of Science:

• Aerospace Medicine
• Health Informatics
• Computational Dentistry

Background:

• Long-term space missions necessitate robust healthcare solutions for astronauts.
• Limited crew and equipment in deep space pose challenges for clinical procedures.
• Standardized workflows are crucial for ensuring astronaut safety and quality care.

Purpose of the Study:

• To develop and represent aerospace dental workflows for space missions.
• To utilize Business Process Modeling and Notation (BPMN) and OWL2 for modeling.
• To create computable knowledge graphs for dental procedures in space.

Main Methods:

• Developed BPMN process models for aerospace dental workflows.
• Created ontology-based knowledge graphs using OWL2.
• Employed a semi-automated processing pipeline for model development.
• Generated knowledge graphs for specific dental conditions: abscess, extractions, dislodged restorations, and trauma.

Main Results:

• Four OWL2-based knowledge graphs were produced, detailing semantic descriptions of key dental procedures.
• The models represent individual dental procedures for low-earth orbit and deep space missions.
• The knowledge graphs are computable, enabling integration into software applications.

Conclusions:

• The developed OWL2 knowledge graphs provide a foundation for decision support systems in space.
• These models can enhance remote clinical tools for astronaut dental care.
• Future work includes expanding the knowledge graphs and developing applications for space healthcare.