This matter of fact also applies to clinical practice guidelines (CPGs). As many medical documents, CPGs are written in a narrative speech as well, without regards to a computer-assisted processing. For the implementation of CPGs in medical facilities an automated processing is therefore desirable. An important fact is that a lot of information in CPGs is provided in a negated form, expressing that certain circumstances in patients or treatments are not available, existing or applicable. Although negated, this information is nevertheless very useful, since it can express the absence of certain conditions or diseases in patients. Moreover, negations can describe which treatment options should not be taken into account for a given patient, helping a practising physician or nurse in his/her decision process for the assortment of a proper treatment. Thus, a proper Negation Detection in CPGs is an important task for the automated processing of this type of medical documents. It helps to accelerate the decision making process and can support medical staff in their care for patients.
We developed algorithms capable of Negation Detection in CPGs. We use syntactical methods provided by the English language to achieve a precise detection of occuring negations. According to our results we are convinced that the involvement of syntactical methods can improve Negation Detection, not only in medical writings but also in arbitrary narrative texts.
Evaluation & Results:
Using NegHunter can support an automated structuring of the information in order to, for instance, decide which therapies or drug regimens are best applied in patients with certain diseases and which are not recommended. This helps to sort out treatment options and supports the medical personnel as well as patients in their decision-making.
Additionally, NegHunter's negation classification allows users to augment the
trigger set by themselves. Therefore, new triggers need to be assigned a negation class.
NegHunter applies its rule base to these new triggers. This makes NegHunter portable
to be applied on other document types as well as extensible and maintainable.
Negation Detection in Medical Documents Using Syntactical Methods
Master's Thesis, Vienna University of Technology, Institute of Software Technology and Interactive Systems, Vienna,
S. Gindl, K. Kaiser, and S. Miksch. Syntactical negation detection in clinical practice guidelines In Proc. of the Conference on Medical Informatics Europe (MIE'08), Göteborg, Sweden, 187-192, IOS Press, 2008.
S. Gindl. Negation Detection in Automated Medical Applications Vienna University of Technology, Institute of Software Technology and Interactive Systems, Vienna, Technical Report, Asgaard-TR-2006-1, p. 28, 2006.
|Downloads|| Master Thesis Poster (PDF, 356KB)
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Facilitating Evidence-based Decision Support Using Information Extraction and Clinical Guidelines
Parts of this project were supported by "Fonds zur Förderung der wissenschaftlichen Forschung FWF" (Austrian Science Fund), grants P15467-INF and L290-N04.