Guideline documents are edited using GEM Cutter to form GEM Documents. These are then uploaded to a repository using a program to extract the essential elements. The next step is to run Apache cTAKES, which is an UIMA-based NLP processor for clinical documents, that forms annotations for the guideline text. These annotations, which include UMLS codes, are then stored in the repository for later retrieval. The final step is to create SVM classifiers based on training sets created by clinical experts. Examples are being designed and developed and will be made available as soon as they are ready.