Formalising and acquiring model-based hypertext in medicine: an integrative approach

Area of Science:

• Medical Informatics
• Knowledge Representation
• Hypermedia Systems

Background:

• Scientific medical information requires robust methods for acquisition, maintenance, and browsing.
• Existing systems may struggle with complex data structures like cyclic data and nested objects.
• Physician access to relevant medical knowledge needs to be context-sensitive.

Purpose of the Study:

• To present an approach for acquiring, maintaining, and browsing scientific medical hypertext.
• To introduce a hypergraph-based data model supporting complex data structures and time-dependent processes.
• To enable context-sensitive presentation of medical knowledge tailored to physician needs.

Main Methods:

• Utilizing a knowledge acquisition methodology combined with a powerful hypergraph-based data model.
• Implementing a rule-based query and modification language for view mechanisms.
• Applying the approach to develop an authoring and tutoring environment for a hypermedia reference book.

Main Results:

• The hypergraph data model effectively handles cyclic data structures and nested objects.
• Path declaration elegantly represents time-dependent medical processes and hypertext tours.
• View mechanisms facilitate context-sensitive medical knowledge presentation.
• The system was successfully applied to a cerebrovascular disease reference tool (NeuroN).

Conclusions:

• The proposed approach enhances the acquisition, maintenance, and browsing of scientific medical hypertext.
• The hypergraph data model offers flexibility and expressive power for medical knowledge representation.
• Context-sensitive views improve the utility of medical information for physicians.