The enormous growth of the world wide flood of information makes it more and more impor-tant to use effective tools to extract and condense key information. There are ongoing re-searches in the branch of Natural Language Processing (NLP). Information Extraction (IE) is a section of NLP and is used to extract information from text to fill a database. However, there are limitations in the use of IE. The IE systems need to be specialised on a specific domain and therefore they are only able to handle text from an indicated domain. IE systems are con-sisting of several components, one of the important components may be composed of termi-nologies, ontologies, and vocabularies.
The UMLS combines a huge variety of source vocabularies, terminologies, and ontologies to the SPECIALIST lexicon, the Metathesaurus, and the Semantic Network. The UMLS is a gi-gantic knowledge base, which covers numerous themes in medicine. Due the large size of umls, it is difficult to extract information. Also matching concepts to phrases is not an easy task. With the help of MMTx the matching problem can be outsourced. To break down the complex data structure of UMLS and MMTx, a more simple and easy ac-cessible data structure was introduced, which is part of the UMLSint package. The UMLSint package was developed to simplify the access to the UMLS data, to extract the attributes, which are of interest, and to analyse the input data to find the referring concepts in the knowl-edge base. The UMLSint package gets as an input a sentence of medical text and returns at-tributes of interest from the UMLS in accordance to questioned phrase. The information con-sists of factual knowledge from the Metathesaurus and information generated by the MetaMap Transfer (MMTx) tool. The MMTx tool is used to create logical elements and gather informa-tion about the lexical and morphological structure. For each logical element various information is now accessible, such as semantic type, term type, Part-Of-Speech tag, Metathesaurus concept ID, and many more. This information can be used for both NLP and IE systems for further analysis of the text. The subject of this thesis is to enable IE systems, which process medical text, an easier access to the knowledge base named Unified Medical Language System (UMLS).Information Extraction
Advisor
Co-Advisor
Keywords
Abstract
Year of Publication
2007
Secondary Title
Institute of Visual Computing and Human-Centered Technology
Paper
Number of Pages
88
reposiTUm Handle
20.500.12708/14604
Publisher
TU Wien
Place Published
Vienna
M. Kohler, “UMLS for information extraction”, Institute of Visual Computing and Human-Centered Technology. TU Wien, Vienna, p. 88, 2007.
Master Thesis
AC05034616
Advisor
Co-Advisor
Keywords
Abstract
It is a complex but very important task to formalize a clinical practice guideline and model it into a representation that can be executed and utilized automatically, since the automatic execution of guidelines at the point-of-care ensures the best available scientic evidence without interrupting clinical work ow. Before a medical text can be translated into such a model, it is necessary to pre-process the text, so that its contents, i. e. the existing medical concepts, can be identied and described unambiguously in order to ensure a correct interpretation.
Semantic Annotation Systems extract medical concepts from the text of guidelines and map them to concepts from medical terminologies, such as the UMLS®Metathesaurus®[36], which contain important additional information. Due to the ambiguity of free text, the correct and automatic identication of medical concepts and the corresponding mapping generated with the help of these systems will probably never be completely correct. Since medical care is an extremely sensitive discipline, the complete reliability of results is crucial for their usability for further processing, which makes it absolutely necessary for experts to controll these results and to modify them, if necessary. This fact led me to develop an editor for the MetaMap Transfer (MMTx) program [2, 3] that enables experts in medical science to solve this task without requiring special skills in information processing. MapFace was designed to realize an easy way to edit the MetaMap results as well as to provide access to all assigned information by a single "mouse-click" in combination with a clearly arranged visualization of the acquired information.Year of Publication
2008
Secondary Title
Institute of Visual Computing and Human-Centered Technology
Paper
Number of Pages
88
reposiTUm Handle
20.500.12708/11035
Publisher
TU Wien
Place Published
Vienna
URL
https://web.archive.org/web/20210417094637/http://ieg.ifs.tuwien.ac.at/projects/mapface/
T. Gschwandtner, “MapFace - a graphical editor for MetaMap Transfer (MMTx)”, Institute of Visual Computing and Human-Centered Technology. TU Wien, Vienna, p. 88, 2008.
Master Thesis
AC05037162
B. Yildiz, “Ontology-driven information extraction”, TU Wien, Vienna, 2007.
B. Yildiz and Miksch, S., “Motivating Ontology-Driven Information Extraction”, in International Conference on Semantic Web and Digital Libraries, (ICSD-2007), 2007.
K. Kaiser and Miksch, S., “Modeling Treatment Processes Using Information Extraction”, in Computational Intelligence In Healthcare (SCI), vol. 48, Springer, 2007, pp. 189–224.