NLP systems for use in medical applications bring new problems not considered by classical methods. Broadly speaking medical texts have three genres: published papers, clinical reports, clinical notes.

Information Extraction (IE) and Questions Answering (AQ) are [...]]]> Speaker: Jon Patrick (University of Sydney)
Date: February 12, 2010
Time: 16:00
Where: Computer Science Faculty, room 3.17 .

NLP systems for use in medical applications bring new problems not considered by classical methods. Broadly speaking medical texts have three genres: published papers, clinical reports, clinical notes.

Information Extraction (IE) and Questions Answering (AQ) are the most common needs for NLP by clinical staf. Published papers are amenable to classical methods apart from needing coverage for many specialised terms. Clinical reports bring new problems due to the use of a specialised clinical terms, highly stylised content for scores, weights and measures and to a lesser degree a specialised grammatical structure. Clinical notes have these problems but many more, such as acronyms, neologisms, personal abbreviations, a high level of spelling errors due to mistyping and second language speakers, poor grammatical structure, multiple authors of the one document.

It is important to overcome these limitations in the text as they represent a large proportion of the content, up to 30%, and to reach the ultimate processing objective of achieving very high accuracy, say 95+% for information extraction, given that people’s lives depend on decisions made at the bedside using our tools.

We have designed a software architecture to tackle these problems whereby incrementally new knowledge discovered about the text is immediately fed back into the knowledge resources of the language processing system, so that it is continually improved at each phase of the processing.

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