. Health-education documents can be much more effective in achieving patient compliance if they are customized for individual readers. For this purpose, a medical record can be thought of as an extremely detailed user model of a reader of such a document. The HealthDoc project is developing methods for producing health-information and patienteducation documents that are tailored to the individual personal and medical characteristics of the patients who receive them. Information from an on-line medical record or from a clinician will be used as the primary basis for deciding how best to fit the document to the patient. In this paper, we describe our research on three aspects of the project: the kinds of tailoring that are appropriate for health-education documents; the nature of a tailorable master document, and how it can be created; and the linguistic problems that arise when a tailored instance of the document is to be generated. 1 The Value of Tailored Health-Education Documents He...

In order to support the efficient development of NL generation systems, two orthogonal .methods are currently purqued with emphasis: (i) reusable, general, and linguistically'motivated surface realization components, and (2) Simple, task-oriented template-based techniques. In this paper we argue that, from an application-oriented perspective, the benefits of both are still .limited. /a order to improve this situation, we suggest and evaluate shallow generation methods associated with increased flexibility. We advise a close connection between domain-motivated and linguistic ontologies that Supports the quick adaptation to new tasks and domains, rather than the reuse of general resources. Our method is especially designed for .generating reports with limited linguistic variations.

The HealthDoc project aims to provide a comprehensive approach to the customization of patient-information and health-education materials through the development of sophisticated natural language generation systems. We adopt a model of patient education that takes into account patient information ranging from simple medical data to complex cultural beliefs, so that our work provides both an impetus and testbed for research in multicultural health communication. We propose a model of language generation, `generation by selection and repair ', that relies on a `master-document' representation that pre-determines the basic form and content of a text, yet is amenable to editing and revision for customization. The implementation of this model has so far led to the design of a sentence planner that integrates multiple complex planning tasks and a rich set of ontological and linguistic knowledge sources. 1 Customizing patient-education material Present-day health-education and patient-infor...

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We discuss a task requiring the coherent presentation of heterogeneous information about objects recorded in electronic catalogues. We consider the advantages of combining hypermedia delivery with natural language generation technology, so as to allow us to view a session with such a system as a coherent conversation or dialogue. We describe two prototype systems we have built which make use of these combined techniques, and focus on those aspects of the systems which attempt to provide coherence. Although the techniques themselves are not novel, their combination is relatively recent, and promises to help forge useful tools for accomplishing our specific information retrieval task. Keywords hypermedia, natural language generation, information presentation, discourse coherence, adaptive hypertext The support of the Economic and Social Research Council for hcrc is gratefully acknowledged. The ilex project is funded by uk Engineering and Physical Sciences Research Council, through gra...

Tailored patient information (TPI) systems are computer programs which produce personalised heath-information material for patients. TPI systems are of growing interest to the natural-language generation (NLG) community; many TPI systems have also been developed in the medical community, usually with mail-merge technology. No matter what technology is used, experience shows that it is not easy to field a TPI system, even if it is shown to be effective in clinical trials. In this paper we discuss some of the difficulties in fielding TPI systems.

This paper is merely an attempt to build up a canvas of issues, on which further discussion is needed.

The exponentially increasing amount of information being made available world-widely through a basic form of hypertext technology creates problems and opportunities for advancing the state of the art in human-machine interaction. The major problem is for users to find their way in this heterogeneous hyperspace and to quickly access the information they need, the major opportunity is the connectivity and interactivity between a substantial population and a considerable amount of resources and knowledge. In this paper we present, through an example application, a novel way of supporting the user of a WWW application. The approach is novel in that we generate a context-sensitive meta-text to serve as a navigation guide for an existing application (rather than to make the application itself user 2 adaptive) and that we use a very dynamic user model, trying to capture the user's minute-to-minute evolving context. We finally discuss how the foundations of this approach fit into a radically new way of supporting humans in information-rich real-world situations, called co-habited mixed reality.

Tailored patient information (TPI) systems are computer programs which produce personalised heath-information material for patients. TPI systems are of growing interest to the natural-language generation (NLG) community; many TPI systems have also been developed in the medical community, usually with mail-merge technology. No matter what technology is used, experience shows that it is not easy to field a TPI system, even if it is shown to be effective in clinical trials. In this paper we discuss some of the difficulties in fielding TPI systems. This is based on our experiences with 2 TPI systems, one for generating asthmainformation booklets and one for generating smoking-cessation letters. 1