Extraction and use of linguistic patterns for modelling medical guidelines.
OBJECTIVE
The quality of knowledge updates in evidence-based medical guidelines can be improved and the effort spent for updating can be reduced if the knowledge underlying the guideline text is explicitly modelled using the so-called linguistic guideline patterns, mappings between a text fragment and a formal representation of its corresponding medical knowledge.


METHODS
Ontology-driven extraction of linguistic patterns is a method to automatically reconstruct the control knowledge captured in guidelines, which facilitates a more effective modelling and authoring of medical guidelines. We illustrate by examples the use of this method for generating and instantiating linguistic patterns in the text of a guideline for treatment of breast cancer, and evaluate the usefulness of these patterns in the modelling of this guideline.


RESULTS
We developed a methodology for extracting and using linguistic patterns in guideline formalization, to aid the human modellers in guideline formalization and reduce the human modelling effort. Using automatic transformation rules for simple linguistic patterns, a good recall (between 72% and 80%) is obtained in selecting the procedural knowledge relevant for the guideline model, even though the precision of the guideline model generated automatically covers only between 20% and 35% of the human-generated guideline model. These results indicate the suitability of our method as a pre-processing step in medical guideline formalization.


CONCLUSIONS
Modelling and authoring of medical texts can benefit from our proposed method. As pre-requisites for generating automatically a skeleton of the guideline model from the procedural part of the guideline text, to aid the human modeller, the medical terminology used by the guideline must have a good overlap with existing medical thesauri and its procedural knowledge must obey linguistic regularities that can be mapped into the control constructs of the target guideline modelling language.
Serban
Radu
R
Artificial Intelligence Department, Vrije Universiteit, De Boelelaan 1081a, 1081HV Amsterdam, The Netherlands. serbanr@few.vu.nl
ten Teije
Annette
A
van Harmelen
Frank
F
Marcos
Mar
M
Polo-Conde
Cristina
C
D001185:Artificial Intelligence; D017408:Guidelines as Topic; D006801:Humans; D019359:Knowledge; D008037:Linguistics; D008511:Medicine / Q000592:standards*; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results