title: Prediction in annotation based guideline encoding.
abstract: The encoding of clinical practice guidelines into machine operable representations poses numerous challenges and will require considerable human intervention for the foreseeable future. To assist and potentially speed up this process, we have developed an incremental approach to guideline encoding which begins with the annotation of the original guideline text using markup techniques. A modular and flexible sequence of subtasks results in increasingly inter-operable representations while maintaining the connections to all prior source representations and supporting knowledge. To reduce the encoding bottleneck we also employ a number of machine-assisted learning and prediction techniques within a knowledge-based software environment. Promising results with a straightforward incremental learning algorithm illustrate the feasibility of such an approach.
authors.affiliation: University of Medicine and Dentistry of New Jersey, New Brunswick, NJ, USA.
authors.forename: C Greg
authors.initials: CG
authors.lastname: Hagerty
authors.forename: David S
authors.initials: DS
authors.lastname: Pickens
authors.forename: Jaime
authors.initials: J
authors.lastname: Chang
authors.forename: Casimir A
authors.initials: CA
authors.lastname: Kulikowski
authors.forename: Frank A
authors.initials: FA
authors.lastname: Sonnenberg
mesh_terms: D000043:Abstracting and Indexing / Q000379:methods* / Q000592:standards; D000465:Algorithms; D001185:Artificial Intelligence; D005568:Forms and Records Control / Q000592:standards*; D006801:Humans; D019146:Hypermedia; D008037:Linguistics; D017410:Practice Guidelines as Topic / Q000592:standards; D011381:Programming Languages; D017432:Unified Medical Language System