Temporal Inferences in Medical Texts

TEMPORAL I]~'RRI~C~S IN HEDICAL TEXTS Klaus K. Obermeier BatteIle's Columbus Laboratories 505 K~ng Avenue CoLumbus, Oh£o 43201-2693, USA ABSTRACT The objectives of this paper are twofold, whereby the computer program is meant to be a particular implementation of a general natural Language [NL] proeessin~ system [NI,PSI which could be used for different domains. The first obiective is to provide a theory for processing temporal information contained in a well-struct- ured, technical text. The second obiective is to argue for a knowledge-based approach to NLP in which the parsing procedure is driven bv extra Linguistic knowledRe. The resulting computer program incorporates enough domain-specific and ~enera[ knowledge so that the parsing procedure can be driven by the knowledge base of the program, while at the same time empLoyin~ a descriptively adequate theory of syntactic processing, i.e., X-bar syntax. My parsing algorithm not only supports the prevalent theories of knowledge-based parsin~ put forth in A[, but also uses a sound linguistic theory for the necessary syntactic information processing. l.O INTRODUCTION This paper describes the development of a NiPS for analyzing domain-specific as well as temporal information in a well-defined text type. The analysis, i.e. output, of the NLPS is a data structure which serves as the input to an expert system. The ultimate Real is to allow the user of the expert system to enter data into the system by means of NL text which follows the linguistic conventions of English. The particular domain chosen to illustrate the underlying theory of such a system ts that of medical descriptive re×is which deal with patients' case histories of Liver diseases. The texts are taken unedtted from the Jourmal of the Amerzcan Medical As~oc£ation. The infor- mation contained in those texts serves as input to PATREC, an intelligent database assistant for MDX, the medical expert system [Chandrasekaran 831. The objectives of this research are twofold, whereby the sy~;tem described above is meant to be a particular implementation of a genera[ NLP which could be used for a variety of domains. The first objective is to provide a theory for processing temporal information contained in a given text. The second objective is to argue for a knowledge-based approach to NL processing in which the parsing procedure is driven by extra Linguistic knowledge. My NLPS, called GROK, [Gran~nattcal Representation of Obiective Knowledge] is a functioning program which is implemented in EL[SP and EFRL on a DEC20/60. The full documentation, including source code is available IObermeier 8A]. The program performs the following tasks: (L) parse a text from a medical iournaL while using Linguistic and extra Linguistic knowledge; (2) map the parsed Linguistic structure into an event-representation; (3) draw temporal and factual inferences within the domain of Liver diseases; (4) create and update a database containing the pertinent information about a patient. 2.0 OVERVI RW 2. l A SampLe Text: The user of my NLPS can enter a text of the format given in FiRure L L The texts which the NLPS accepts are descriptive for a particular …

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