Dutch Sublanguage Semantic Tagging combined with Mark-Up Technology

Dutch Sublanguage Semantic Tagging combined with Mark-Up Technology P. Spyns [1], NT. Nhhn [2], E. Baert [1], N. Sager [2], G. De Moor [1] (1) Division of Medical Informatics, University Hospital Gent De Pintelaan 185 (5K3), B-9000 Gent, Belgium (2) Courant Institute of Mathematical Sciences, New York University 251 Mercer Street, NY 10012, New York, USA {Peter. Spyns, Erik. Baert, Georges. DeMoor}@rug. ac. be, {nhan, sager}@cs, nyu. edu Abstract In this paper, we want to show how the morphological component of an existing NLP-system for Dutch (Dutch Medical Language Processor - DMLP) has been ex- tended in order to produce output that is compatible with the language independent modules of the LSP-MLP system (Linguis- tic String Project - Medical Language Pro- cessor) of the New York University. The former can take advantage of the language independent developments of the latter, while focusing on idiosyncrasies for Dutch. This general strategy will be illustrated by a practical application, namely the high- lighting of relevant information in a pa- tient discharge summary (PDS) by means of modern HyperText Mark-Up Language (HTML) technology. Such an application can be of use for medical administrative purposes in a hospital environment. 1 Introduction Medical patient reports consist mainly of free text. While numerical data can be stored and processed (relatively) easily, free text is rather difficult to pro- cess by a computer, although in many cases it con- tains the most relevant information. The use of natural language does not facilitate the automation of. these tasks and hinders access to the wealth of medical information. However, natural lan- guage still is the most frequently used and easiest way to transmit complex messages (Scherrer et ah, 1989). Hence, some authors consider the study and application of Natural Language Processing (NLP) in Medicine (Scherrer et" al., 1989), (McCray et ah. 1995), (Chute, 1997) as one of the most challenging issues in the field of medical information retrieval (Baud et al., 1992a), (Friedman and Johnson, 1992). Up till now, not many NLP-driven systems have ac- tually been implemented (Spyns, 1996b). A concise overview of NLP-based information retrieval tech- niques for clinical narrative can be found in (Hersh, 1996, chapter 11, pp. 211-323). A possible environment for (medical) information retrieval is the Medical Registration Department of a hospital, and more in particular the medical en- coding service. Clinical data in free text format are replaced by a set of numerical codes that summarise the content of the entire document. In general, the patient discharge summary (PDS), being a synthe- sis of the patient stay, is used for the encoding and abstracting task instead of the entire medical record (Duisterhout, 1996). An important aspect of med- ical encoding consists of a thorough review of the PDS in order to discover the relevant words (diag- noses, surgical deeds, interventional equipment etc.) (Bowman, 1996, p.216). The aim of the NLP-based HTML application presented below is to speed up the reviewing process by displaying a PDS and high- lighting the keywords. The following sections provide …

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