From data to text in the Neonatal Intensive Care Unit: using NLG technology for decision support and information management

Albert Gatt, Francois Portet, Ehud Reiter, Jim Hunter, Saad Mahamood, Wendy Moncur, Somayajulu Sripada

    Research output: Contribution to journalReview article

    49 Citations (Scopus)

    Abstract

    Contemporary Neonatal Intensive Care Units collect vast amounts of patient data in various formats, making efficient processing of information by medical professionals difficult. Moreover, different stakeholders in the neonatal scenario, which include parents as well as staff occupying different roles, have different information requirements. This paper describes recent and ongoing work on building systems that automatically generate textual summaries of neonatal data. Our evaluation results show that the technology is viable and comparable in its effectiveness for decision support to existing presentation modalities. We discuss the lessons learned so far, as well as the major challenges involved in extending current technology to deal with a broader range of data types, and to improve the textual output in the form of more coherent summaries.

    Original languageEnglish
    Pages (from-to)153-186
    Number of pages34
    JournalAI Communications
    Volume22
    Issue number3
    DOIs
    Publication statusPublished - 2009

    Keywords

    • Natural language generation
    • signal analysis
    • signal processing
    • knowledge-based systems
    • medical informatics
    • narrative
    • WEATHER FORECASTS
    • SITUATION MODELS
    • CLINICAL-DATA
    • TIME
    • LANGUAGE
    • SYSTEMS
    • WORDS
    • TRIAL
    • VISUALIZATION
    • COMPREHENSION

    Cite this

    Gatt, Albert ; Portet, Francois ; Reiter, Ehud ; Hunter, Jim ; Mahamood, Saad ; Moncur, Wendy ; Sripada, Somayajulu. / From data to text in the Neonatal Intensive Care Unit : using NLG technology for decision support and information management. In: AI Communications. 2009 ; Vol. 22, No. 3. pp. 153-186.
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    abstract = "Contemporary Neonatal Intensive Care Units collect vast amounts of patient data in various formats, making efficient processing of information by medical professionals difficult. Moreover, different stakeholders in the neonatal scenario, which include parents as well as staff occupying different roles, have different information requirements. This paper describes recent and ongoing work on building systems that automatically generate textual summaries of neonatal data. Our evaluation results show that the technology is viable and comparable in its effectiveness for decision support to existing presentation modalities. We discuss the lessons learned so far, as well as the major challenges involved in extending current technology to deal with a broader range of data types, and to improve the textual output in the form of more coherent summaries.",
    keywords = "Natural language generation, signal analysis, signal processing, knowledge-based systems, medical informatics, narrative, WEATHER FORECASTS, SITUATION MODELS, CLINICAL-DATA, TIME, LANGUAGE, SYSTEMS, WORDS, TRIAL, VISUALIZATION, COMPREHENSION",
    author = "Albert Gatt and Francois Portet and Ehud Reiter and Jim Hunter and Saad Mahamood and Wendy Moncur and Somayajulu Sripada",
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    From data to text in the Neonatal Intensive Care Unit : using NLG technology for decision support and information management. / Gatt, Albert; Portet, Francois; Reiter, Ehud; Hunter, Jim; Mahamood, Saad; Moncur, Wendy; Sripada, Somayajulu.

    In: AI Communications, Vol. 22, No. 3, 2009, p. 153-186.

    Research output: Contribution to journalReview article

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    T2 - using NLG technology for decision support and information management

    AU - Gatt, Albert

    AU - Portet, Francois

    AU - Reiter, Ehud

    AU - Hunter, Jim

    AU - Mahamood, Saad

    AU - Moncur, Wendy

    AU - Sripada, Somayajulu

    PY - 2009

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    AB - Contemporary Neonatal Intensive Care Units collect vast amounts of patient data in various formats, making efficient processing of information by medical professionals difficult. Moreover, different stakeholders in the neonatal scenario, which include parents as well as staff occupying different roles, have different information requirements. This paper describes recent and ongoing work on building systems that automatically generate textual summaries of neonatal data. Our evaluation results show that the technology is viable and comparable in its effectiveness for decision support to existing presentation modalities. We discuss the lessons learned so far, as well as the major challenges involved in extending current technology to deal with a broader range of data types, and to improve the textual output in the form of more coherent summaries.

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    KW - narrative

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    KW - SITUATION MODELS

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    KW - LANGUAGE

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    KW - TRIAL

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