Interactive Media Systems, TU Vienna

Negation Detection in Medical Documents Using Syntactical Methods

Thesis by Stefan Gindl

Supervision by Katharina Kaiser and Silvia Miksch


Medical information is often stored in a narrative way, which makes the automated processing a difficult and time-consuming task. Persons responsible for the authoring of medical documents do not take care of a further processing with automated systems. So, information stored in medical writings is not directly usable for the processing with computers. Due to this, efforts have been made to transfer these narrative documents in a format easier processable with computers.

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 occurring 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.


S. Gindl: "Negation Detection in Medical Documents Using Syntactical Methods"; Supervisor: K. Kaiser, S. Miksch; Institut für Softwaretechnik und Interaktive Systeme, 2008; final examination: 03-31-2008.


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