Medical WordNet: A New Methodology for the Construction and Validation of Information Resources for Consumer Health

A consumer health information system must be able to comprehend both expert and nonexpert medical vocabulary and to map between the two. We describe an ongoing project to create a new lexical database called Medical WordNet (MWN), consisting of medically relevant terms used by and intelligible to non-expert subjects and supplemented by a corpus of natural-language sentences that is designed to provide medically validated contexts for MWN terms. The corpus derives primarily from online health information sources targeted to consumers, and involves two sub-corpora, called Medical FactNet (MFN) and Medical BeliefNet (MBN), respectively. The former consists of statements accredited as true on the basis of a rigorous process of validation, the latter of statements which non-experts believe to be true. We summarize the MWN / MFN / MBN project, and describe some of its applications. 1 From WordNet to Medical WordNet WordNet is the principal lexical database used in natural language processing (NLP) research and applications.  In addition, input includes sentences derived from online medical information services targeted to consumers. Our methodology is designed (1) to document natural language sentential contexts for each relevant word sense in such a way that the expressed information can be (2) validated by medical experts and (3) accessed automatically by NLP applications such as information retrieval, machine translation, question-answer systems, and text summarization. A major stumbling block for existing NLP applications is automatic sense disambiguation. An automatic system can detect with high reliability that a given occurrence of a word like feel or dead is a verb or adjective. But it cannot easily determine which of a variety of alternative meanings such polysemous words have in any given context. WordNet’s architecture, designed for representing and distinguishing word senses, has made an important contribution towards a solution of the automatic word sense disambiguation problem. Our corpus of English language sentences relating to medical phenomena is designed to build upon this contribution. The corpus is restricted to grammatically complete, syntactically simple sentences in natural language which have been rated as understandable by non-expert human subjects in controlled questionnaire-based experiments. It is restricted in addition to sentences which are self-contained in the sense that they make no reference to any prior context and do not contain any proper names, or anaphoric elements (like it or he or then) that need to be interpreted with respect to other sentences or some surrounding discourse or context. This corpus is designed to be used initially for purposes of quality assurance of MWN and also to support the population of MWN by yielding new families of words and word senses for inclusion. As will become clear, however, our use of human validators will allow us to extend the usefulness of the corpus in a variety of ways. Thus we can use it to build new sorts of applications for information retrieval in the domain of consumer health. But it also allows new avenues of research in linguistics and psychology, for example in allowing us to explore individual and group differences in medical knowledge and vocabulary, and in understanding non-expert medical reasoning and decision-making. 2 Medical FactNet and Medical BeliefNet To this end, however, we need to exploit our validation data to create two sentential subcorpora, called Medical FactNet (MFN) and Medical BeliefNet (MBN), respectively. MFN consists of those sentences in the corpus which receive high marks for correctness on being assessed by medical experts. MFN is thus designed to constitute a representative fraction of the true beliefs about medical phenomena which are intelligible to non-expert English-speakers. MBN consists of those sentences in the corpus which receive high marks for assent. MBN is thus designed to constitute a representative fraction of the beliefs about medical phenomena (both true and false) distributed through the population of English speakers. The validation process that is involved in the construction of MFN is used to detect errors in the existing WordNet, and also to ensure that the coverage of the natural language medical lexicon in MWN is of a scientific level sufficient to allow MWN technology to work in tandem with terminology and ontology systems designed for use by experts. Both MFN and MBN inherit from MWN the formal architecture of the Princeton WordNet. (Fellbaum, ed., 1998) However, we enhance this architecture to maximize its usefulness in medical information retrieval. Compiling MFN and MBN in tandem allows systematic assessment of the disparity between lay beliefs and vocabulary as concerns medical phenomena and the corresponding expert medical knowledge. The ultimate goal of our work on MFN is to document the entirety of the medical knowledge that can be understood by average adult consumers of healthcare services in the United States today. If the methodology for the creation and validation of the corpus here described proves successful, then we believe that the preconditions for the realization of this much larger goal will have been established. Responses from NLP researchers and from online information providers to our initial work on MFN/MBN convinces us that this realization would have considerable significance for the management and retrieval of consumer health information in the future. 3  Recent studies of the use of computer-based tools for consumer health information retrieval point to a mismatch between existing tools and the non-expert language used by most consumers – the language used not only by patients but also by family members, advisors, administrators, lawyers, and so forth, and to some degree also by nurses and physicians.  Where the usage of medical terms by professionals is at least in principle subject to control by standardization efforts, the highly contextually dependent usage of medical terms on the part of lay persons is much more difficult to capture in applications – and this in spite of the fact that it is in many ways simpler than expert usage. The taxonomies reflecting popular lexicalizations in all domains are indeed much less elaborate at both the upper and lower levels than in the corresponding technical lexica. (Medin and Atran, eds., 1999) Thus there are no popular terms linking infectious disease and mumps, so that in the popular medical taxonomy of diseases the former immediately subsumes the latter. The popular medical vocabulary naturally covers only a small segment of the encyclopedic vocabulary of medical professionalsm, and it lexicalizes mainly at the level of taxonomic orders. Popular medical terms (flu) are often fuzzier than technical medical terms. Many popular terms also cover a larger range of referent types than do technical terms; others may cover only part of the extension of their technical counterparts. We hypothesize, however, that with few exceptions the focal meanings (Berlin and Kay, 1969) of expert and non-expert terms will be identical. Constructing MFN and MBN allows us to test this and related hypotheses in a systematic way. 4 Mismatches in Doctor-Patient Communication The skills of a physician in general practice comprise the ability to acquire relevant and reliable information through communication with patients through the use of non-expert language and to convey diagnostic and therapeutic information in ways tailored to the individual patient. Since the physician, too, is a member of the wider community of non-experts, and continues to use the non-expert language for everyday purposes, one might assume that there are no difficulties in principle keeping him from being able to formulate medical knowledge in a vocabulary that the patient can understand. As (Slaughter, 2002) and (C. A. Smith, et al., 2002) have shown, however, there are limits to this competence. The former examines dialogue between physicians and patients in the form of question-answer pairs, focusing especially on the relations documented in the UMLS Semantic Network. Only some 30% of the relations used by professionals in their answers directly match the relations used by consumers in formulating their questions. An example of one such questionanswer pair is taken from (Slaughter, p. 224):  My seven-year-old son developed a rash today that I believe to be chickenpox. My concern is that a friend of mine had her 10-day-old baby at my home last evening before we