Biomedical subjects
Publications and source records attributed to P Spyns.
This paper provides a preliminary evaluation of a general Dutch medical language processor (DMLP). Four examples of different potential applications (based on different linguistic modules) are presented, each with its own evaluation method. Finally, a critical review of the used evaluation methods is offered according to the state of the art in medical language processing.
In this paper, we want to show how an existing morpho-syntactic analyser for Dutch (Dutch Medical Language Processor--DMLP) has been extended in order to produce output that is compatible with the language independent modules of the LSP-MLP system (Linguistic String Project--Medical Language Processor) of the New York University. The former can focus on idiosyncrasies for Dutch and take advantage of the language independent developments of the latter. This general strategy will be illustrated by a practical application, namely the extraction of clinical information from Dutch patient discharge summaries. Such an application can be of use for education, research and quality control purposes in a hospital environment.
In the GALEN project, the syntactic-semantic tagger MultiTALE is upgraded to extract knowledge from natural language surgical procedure expressions. In this paper, we describe the methodology applied and show that out of a randomly selected sample of such expressions, 81% could be analysed correctly. The problems encountered are summarised and areas of further investigation identified.
This paper describes the current state of a medical language processor for Dutch. The goal is to implement a language specific front-end compatible with some existing applications that aim at the intelligent extraction and processing of information from patient discharge summaries. A complete chain for processing and understanding Dutch medical documents will be the ultimate result. The text focuses mainly on the language specific aspects of the language processing chain. Evaluation results of the already functioning components are given as well as an outline for future developments and enhancements. A short theoretical background is provided (cf. also [1-3]: Rossi Mori et al., Proc. SCAMC 90, 1990, pp. 185-189; Wingert, in: Informatics and Medicine, an advanced course, Springer-Verlag. 1977, pp. 579-646; Wingert, Proc. MEDINFO 80, 1980, pp. 1321-1331) before the description of each component in order to familiarise the non-experienced reader with the basic notions of computational linguistics.
An overview is given of natural language processing applications in medicine. An attempt has been made to enumerate the most important and known international projects and to summarize their goals, principles, methods and results. A section is devoted to projects involving the Dutch language. A more general discussion about the two fundamental approaches concerning medical language understanding is provided. An extensive bibliography may be useful for those wishing to explore this research domain.
This paper discusses a medical language processor for Dutch (DMLP). The DMLP forms part of the results of a joint project sponsored by the European Commission, which aims at the intelligent extraction and processing of information of patient discharge summaries. We will focus more specifically on the natural language processing aspects for Dutch which are treated by the K.U. Leuven team.
In a hospital environment, where large numbers of reports are compiled, data compression offers a method for substantially reducing the required disk space whilst protecting the confidentiality of patient data. An analysis is given of the most important parameters that influence compression of free-text data, to realize an efficient implementation of an archiving system for medical reports. An overall reduction of the required disk space by more than 50% can be attained, as well as supplementary level of protection. A slight increase in access time is thereby inevitable, but almost insignificant. Such an archiving system constitutes a necessary component for an intelligent information retrieval system. The access to information contained in medical documents (by means of natural language processing and artificial intelligence techniques) is considered to be one of the key issues in the field of medical informatics for the coming decades.
In the GALEN project, the syntactic-semantic tagger MultiTALE is upgraded to extract knowledge from natural language surgical procedure expressions. In this paper, we describe the methodology applied and show that out of a randomly selected sample of such expressions coming from the procedure axis of Snomed International, 81% could be analysed correctly. The problems encountered fall in three different categories: unusual grammatical configurations within the Snomed terms, insufficient domain knowledge and different categorisation of concepts and semantic links in the domain and linguistic models used. It is concluded that the Multi-TALE system can be used to attach meaning to words that not have been encountered previously, but that an interface ontology mediating between domain models and linguistic models is needed to arrive at a higher level of independence from both particular languages and from particular domains.