View source for Denecke and Bernauer ARTIFICIAL INTELLIGENCE IN MEDICINE 2007

== Citation ==

Kerstin Denecke and Jochen Bernauer. 2007. Extracting Specific Medical Data Using Semantic Structures. Artificial Intelligence in Medicine, LNCS 2007 Volume 4594/2007, 257-264.

== Online version ==

[http://www.springerlink.com/content/n858219p72rm3105/fulltext.pdf SpringerLink]

== Summary ==

The [[Category::paper]] presents a medical [[AddressesProblem::Information Extraction]] system which is based on automatic generation of semantic structures for free-text. The dataset used is a gold standard comprising 50 discharge summaries from a surgical department in a hospital. The purpose of the system is to fill in three templates defined below.

[[File:templates.png]]

Current(2007) approaches to medical NLP are often limited to a certain medical domain and their construction is fairly complicated. In addition, they are limited to a certain language, especially in English. So, the system in this paper is designed to overcome these limitations.

The key method of this system the authors suggest is [[UsesMethod::building semantic structures]] from texts.
A conceptual graph-like representation is produced for each sentence of a text, and then these semantic structures are exploited to extract information.

The system is composed of three major modules: a text-preprocessor, a semantic interpreter, and an extractor.
In the text-preprocessor, sections are detected and classifies, and texts are parsed. This results in tagged texts with dates, quantities and dosage specifications, and expressions with special meaning like "exclusion of" and "evidence of". The semantic interpreter produces a semantic representation consisting of concepts and relations from each sentence. In the extractor, according to manually defined rules, information is extracted from the text itself, and its semantic representation in order to fill in templates.

[[File:semanticRep.png]]


The advantage of this system is that the components that might be adopted for processing texts in another language because it represents texts as semantic structures.
The system achieved 81-95% precision and 83-97% recall of performance on template filling tasks. The main reasons of error were unknown words, wrong paragraph detection, missing trigger words, and processing needs additional knowledge.