MPLUS: a probabilistic medical language understanding system

MPLUS: A Probabilistic Medical Language Understanding System Lee M. Christensen, Peter J. Haug, and Marcelo Fiszman Department of Medical Informatics, LDS Hospital/University of Utah, Salt Lake City, UT E-mail: ldlchris@ihc.com, ldphaug@ihc.com, ldmfiszm@ihc.com Abstract This paper describes the basic philosophy and implementation of MPLUS (M+), a robust medical text analysis tool that uses a semantic model based on Bayesian Networks (BNs). BNs provide a concise and useful formalism for representing semantic patterns in medical text, and for recognizing and reasoning over those patterns. BNs are noise-tolerant, and facilitate the training of M+. 1 2 Introduction In the field of medical informatics, computerized tools are being developed that depend on databases of clinical information. These include alerting systems for improved patient care, data mining systems for quality assurance and research, and diagnostic systems for more complex medical decision support. These systems require data that is appropriately structured and coded. Since a large portion of the information stored in patient databases is in the form of free text, manually coding this information in a format accessible to these tools can be time consuming and expensive. In recent years, natural language processing (NLP) methodologies have been studied as a means of automating this task. There have been many projects involving automated medical language analysis, including deciphering pathology reports (Smart and Roux, 1995), physical exam findings (Lin et al., 1991), and radiology reports (Friedman et al., 1994; Ranum, 1989; Koehler, 1998). M+ is the latest in a line of NLP tools developed at LDS Hospital in Salt Lake City, Utah. Its predecessors include SPRUS (Ranum, 1989) and SymText (Koehler, 1998). These tools have been used in the realm of radiology reports, admitting diagnoses (Haug et al., 1997), radiology utilization review (Fiszman, 2002) and syndromic detection (Chapman et al., 2002). Some of the character of these tools derives from common characteristics of radiology reports, their initial target domain. Because of the off-the-cuff nature of radiology dictation, a report will frequently contain text that is telegraphic or otherwise not well formed grammatically. Our desire was not only to take advantage of phrasal structure to discover semantic patterns in text, but also to be able to infer those patterns from lexical and contextual cues when necessary. Most NLP systems capable of semantic analysis employ representational formalisms with ties to classical logic, including semantic grammars (Friedman et al., 1994), unification- based semantics (Moore, 1989), and description logics (Romacker and Hahn, 2000). M+ and its predecessors employ Bayesian Networks (Pearl, 1988), a methodology outside this tradition. This study discusses the philosophy and implementation of M+, and attempts to show how Bayesian Networks can be useful in medical text analysis. The M+ Semantic Model 2.1 Semantic Bayesian Networks M+ uses Bayesian Networks (BNs) to represent the basic semantic types and relations within a medical domain such as chest radiology reports. M+ BNs are structurally similar to semantic networks, in that they are implemented as directed acyclic graphs, with nodes representing word and concept types, and links representing relations between those types. …

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