Structural variation in generated health reports

We present a natural language generator that produces a range of medical reports on the clinical histories of cancer patients, and discuss the problem of conceptual restatement in generating various textual views of the same conceptual content. We focus on two features of our system: the demand for 'loose paraphrases' between the various reports on a given patient, with a high degree of semantic overlap but some necessary amount of distinctive content; and the requirement for paraphrasing at primarily the discourse level

Summarisation and visualisation of e-Health data repositories

At the centre of the Clinical e-Science Framework (CLEF) project is a repository of well organised, detailed clinical histories, encoded as data that will be available for use in clinical care and in-silico medical experiments. We describe a system that we have developed as part of the CLEF project, to perform the task of generating a diverse range of textual and graphical summaries of a patient’s clinical history from a data-encoded model, a chronicle, representing the record of the patient’s medical history. Although the focus of our current work is on cancer patients, the approach we describe is generalisable to a wide range of medical areas

Intuitive querying of e-Health data repositories

At the centre of the Clinical e-Science Framework (CLEF) project is a repository of well organised, detailed clinical histories, encoded as data that will be available for use in clinical care and in-silico medical experiments. An integral part of the CLEF workbench is a tool to allow biomedical researchers and clinicians to query – in an intuitive way – the repository of patient data. This paper describes the CLEF query editing interface, which makes use of natural language generation techniques in order to alleviate some of the problems generally faced by natural language and graphical query interfaces. The query interface also incorporates an answer renderer that dynamically generates responses in both natural language text and graphics

Lexical Parameters, Based on Corpus Analysis of English and Swedish Cancer Data, of Relevance for NLG

Proceedings of the 16th Nordic Conference of Computational Linguistics NODALIDA-2007. Editors: Joakim Nivre, Heiki-Jaan Kaalep, Kadri Muischnek and Mare Koit. University of Tartu, Tartu, 2007. ISBN 978-9985-4-0513-0 (online) ISBN 978-9985-4-0514-7 (CD-ROM) pp. 333-336

Composing questions through conceptual authoring

This article describes a method for composing fluent and complex natural language questions, while avoiding the standard pitfalls of free text queries. The method, based on Conceptual Authoring, is targeted at question-answering systems where reliability and transparency are critical, and where users cannot be expected to undergo extensive training in question composition. This scenario is found in most corporate domains, especially in applications that are risk-averse. We present a proof-of-concept system we have developed: a question-answering interface to a large repository of medical histories in the area of cancer. We show that the method allows users to successfully and reliably compose complex queries with minimal training

Generic Querying of Relational Databases using Natural Language Generation Techniques

This paper presents a method of querying databases by means of a natural languagelike interface which offers the advantage of minimal configuration necessary for porting the system. The method allows us to first automatically infer the set of possible queries that can apply to a given database, automatically generate a lexicon and grammar rules for expressing these queries, and then provide users with an interface that allows them to pose these queries in natural language without the well-known limitations of most natural language interfaces to databases. The way the queries are inferred and constructed means that semantic translation is performed with perfect reliability

Multi-modal presentation of medical histories

ABSTRACT This paper describes a visualisation architecture that integrates graphical devices and natural language in a cooperative system for navigating through complex images of medical histories. We show how the addition of automatically generated natural language can be used to improve the usability of a graphical user interface and conversely how the graphical user interface can be used to specify the content of user customizable medical reports

Evaluation of the CLEF query interface

As part of the clef project, we have developed a method for allowing subject matter experts to pose complex queries to a database. The method makes use of natural language generation, whereby users compose queries through interaction with a dynamic text that is generated "on the fly". We present here some first-step evaluations that we have conducted with our modest pool of available testers, the numbers of which are constrained for practical reasons having to do with data security. The results allow us to draw some useful conclusions about the usability of the method, and its training requirements, for subjects that are representative of our "typical" intended end-users