Ehud Reiter

AIIM Journal 2025 Journal Article

The role of natural language processing in improving cancer care: A scoping review with narrative synthesis

• Mengxuan Sun
• Ehud Reiter
• Lisa Duncan
• Rosalind Adam

Objectives To review studies of Natural Language Processing (NLP) systems that assist in cancer care, explore use cases and summarise current research progress. Methods A scoping review, searching six databases (1) MEDLINE, (2) Embase, (3) IEEE Xplore, (4) ACM Digital Library, (5) Web of Science, and (6) ACL Anthology. Studies were included that reported NLP systems that had been used to improve cancer management by patients or clinicians. Studies were synthesised descriptively and using content analysis. Results Twenty-nine studies were included. Studies mainly applied NLP in mixed cancer types (n = 10, 34. 48 %) and breast cancer (n = 8, 27. 59 %). NLP was used in four main ways: (1) to support patient education and self-management; (2) to improve efficiency in clinical care by summarising, extracting, and categorising data, and supporting record-keeping; (3) to support prevention and early detection of patient problems or cancer recurrence; and (4) to improve cancer treatment by supporting clinicians to make evidence-based treatment decisions. Studies highlighted a wide variety of use cases for NLP technologies in cancer care. However, few technologies have been evaluated within clinical settings, none have been evaluated against clinical outcomes, and none have been implemented into clinical care. Conclusion NLP has the potential to improve cancer care via several mechanisms, including information extraction and classification, which could enable automation and personalization of care processes. Additionally, NLP tools such as chatbots show promise in improving patient communication and support. However, there are deficiencies in the evaluation and clinical integration challenges. Interdisciplinary collaboration between computer scientists and clinicians will be essential if NLP technologies are to fulfil their potential to improve patient experience and outcomes. Registered Protocol: https: //doi. org/10. 17605/OSF. IO/G9DSR

AIIM Journal 2012 Journal Article

Automatic generation of natural language nursing shift summaries in neonatal intensive care: BT-Nurse

• James Hunter
• Yvonne Freer
• Albert Gatt
• Ehud Reiter
• Somayajulu Sripada
• Cindy Sykes

Introduction Our objective was to determine whether and how a computer system could automatically generate helpful natural language nursing shift summaries solely from an electronic patient record system, in a neonatal intensive care unit (NICU). Methods A system was developed which automatically generates partial NICU shift summaries (for the respiratory and cardiovascular systems), using data-to-text technology. It was evaluated for 2 months in the NICU at the Royal Infirmary of Edinburgh, under supervision. Results In an on-ward evaluation, a substantial majority of the summaries was found by outgoing and incoming nurses to be understandable (90%), and a majority was found to be accurate (70%), and helpful (59%). The evaluation also served to identify some outstanding issues, especially with regard to extra content the nurses wanted to see in the computer-generated summaries. Conclusions It is technically possible automatically to generate limited natural language NICU shift summaries from an electronic patient record. However, it proved difficult to handle electronic data that was intended primarily for display to the medical staff, and considerable engineering effort would be required to create a deployable system from our proof-of-concept software.

AIJ Journal 2009 Journal Article

Automatic generation of textual summaries from neonatal intensive care data

• François Portet
• Ehud Reiter
• Albert Gatt
• Jim Hunter
• Somayajulu Sripada
• Yvonne Freer
• Cindy Sykes

Effective presentation of data for decision support is a major issue when large volumes of data are generated as happens in the Intensive Care Unit (ICU). Although the most common approach is to present the data graphically, it has been shown that textual summarisation can lead to improved decision making. As part of the BabyTalk project, we present a prototype, called BT-45, which generates textual summaries of about 45 minutes of continuous physiological signals and discrete events (e. g. : equipment settings and drug administration). Its architecture brings together techniques from the different areas of signal processing, medical reasoning, knowledge engineering, and natural language generation. A clinical off-ward experiment in a Neonatal ICU (NICU) showed that human expert textual descriptions of NICU data lead to better decision making than classical graphical visualisation, whereas texts generated by BT-45 lead to similar quality decision-making as visualisations. Textual analysis showed that BT-45 texts were inferior to human expert texts in a number of ways, including not reporting temporal information as well and not producing good narratives. Despite these deficiencies, our work shows that it is possible for computer systems to generate effective textual summaries of complex continuous and discrete temporal clinical data.

ECAI Conference 2008 Conference Paper

Using Natural Language Generation Technology to Improve Information Flows in Intensive Care Units

• Jim Hunter
• Albert Gatt
• François Portet
• Ehud Reiter
• Somayajulu Sripada

In the drive to improve patient safety, patients in modern intensive care units are closely monitored with the generation of very large volumes of data. Unless the data are further processed, it is difficult for medical and nursing staff to assimilate what is important. It has been demonstrated that data summarization in natural language has the potential to improve clinical decision making; we have implemented and evaluated a prototype system which generates such textual summaries automatically. Our evaluation of the computer generated summaries showed that the decisions made by medical and nursing staff after reading the summaries were as good as those made after viewing the currently available graphical presentations with the same information content. Since our automatically generated textual summaries can be improved by including additional content and expert knowledge, they promise to enhance information exchange between the medical and nursing staff, particularly when integrated with the currently available graphical presentations. The main feature of this technology is that it brings together a diverse set of techniques such as medical signal analysis, knowledge based reasoning, medical ontology and natural language generation. In this paper we discuss the main components of our approach with a critical analysis of their strengths and limitations and present options for improvement to address these limitations.

AIJ Journal 2005 Journal Article

Choosing words in computer-generated weather forecasts

• Ehud Reiter
• Somayajulu Sripada
• Jim Hunter
• Jin Yu
• Ian Davy

One of the main challenges in automatically generating textual weather forecasts is choosing appropriate English words to communicate numeric weather data. A corpus-based analysis of how humans write forecasts showed that there were major differences in how individual writers performed this task, that is, in how they translated data into words. These differences included both different preferences between potential near-synonyms that could be used to express information, and also differences in the meanings that individual writers associated with specific words. Because we thought these differences could confuse readers, we built our SumTime-Mousam weather-forecast generator to use consistent data-to-word rules, which avoided words which were only used by a few people, and words which were interpreted differently by different people. An evaluation by forecast users suggested that they preferred SumTime-Mousam's texts to human-generated texts, in part because of better word choice; this may be the first time that an evaluation has shown that nlg texts are better than human-authored texts.

AIJ Journal 2005 Journal Article

Connecting language to the world

• Deb Roy
• Ehud Reiter

IJCAI Conference 2005 Conference Paper

Evaluating an NLG System using Post-Editing

• Somayajulu G. Sripada
• Ehud Reiter
• Lezan

Computer-generated texts, whether from Natural Language Generation (NLG) or Machine Translation (MT) systems, are often post-edited by humans before being released to users. The frequency and type of post-edits is a measure of how well the system works, and can be used for evaluation. We describe how we have used post-edit data to evaluate SUMTIME-MOUSAM, an NLG system that produces weather forecasts.

ECAI Conference 2004 Conference Paper

Lessons from Deploying NLG Technology for Marine Weather Forecast Text Generation

• Somayajulu Sripada
• Ehud Reiter
• Ian Davy
• Kristian Nilssen

AIJ Journal 2003 Journal Article

Lessons from a failure: Generating tailored smoking cessation letters

• Ehud Reiter
• Roma Robertson
• Liesl M. Osman

stop is a Natural Language Generation (nlg) system that generates short tailored smoking cessation letters, based on responses to a four-page smoking questionnaire. A clinical trial with 2553 smokers showed that stop was not effective; that is, recipients of a non-tailored letter were as likely to stop smoking as recipients of a tailored letter. In this paper we describe the stop system and clinical trial. Although it is rare for ai papers to present negative results, we believe that useful lessons can be learned from stop. We also believe that the ai community as a whole could benefit from considering the issue of how, when, and why negative results should be reported; certainly a major difference between ai and more established fields such as medicine is that very few ai papers report negative results.

AIIM Journal 2002 Journal Article

The Spoken Language Translator

• Ehud Reiter

IJCAI Conference 1993 Conference Paper

Using Classification as a Programming Language

• Chris Mellish
• Ehud Reiter

Our experience in the IDAS natural language generation project has shown us that IDAS'S KL- ONE-like classifier, originally built solely to hold a domain knowledge base, could also be used to perform many of the computations required by a natural-language generation system; in fact it seems possible to use the classifier to encode and execute arbitrary programs. We discuss IDAS'S classification system and how it differs from other such systems (perhaps most notably in the presence of template' constructs that enable recursion to be encoded); give examples of program fragments encoded in the classification system; and compare the classification approach to other AI programming paradigms (e. g. , logic programming).

AAAI Conference 1990 Conference Paper

Avoiding Unwanted Conversational Implicatures in Text and Graphics

• Joseph Marks
• Ehud Reiter

We have developed two systems, FN and ANDD, that use natural language and graphical displays, respectively, to communicate information about objects to human users. Both systems must deal with the fundamental problem of ensuring that their output does not carry unwanted and inappropriate conversational implicatures. We describe the types of conversational implicatures that FN and ANDD can avoid, and the computational strategies the two systems use to generate output that is free of unwanted implicatures.