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首页> 外文期刊>Applied Cognitive Psychology >When a graph is poorer than 100 words: A comparison of computerised natural language generation, human generated descriptions and graphical displays in neonatal intensive care
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When a graph is poorer than 100 words: A comparison of computerised natural language generation, human generated descriptions and graphical displays in neonatal intensive care

机译：当图形不足100个单词时：新生儿重症监护中计算机化自然语言生成，人工生成的描述和图形显示的比较

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摘要

Volunteer staff from a Neonatal Intensive Care Unit (NICU) were presented with sets of anonymised physiological data recorded over approximately 45 minute periods from former patients. Staff were asked to select medicalursing actions appropriate for each of the patients whose data were displayed. Data were shown in one of three conditions (a) as multiple line graphs similar to those commonly shown on the ward, or as textual descriptions generated by (b) expert medicalursing staff or (c) computerised natural language generation (NLG). An overall advantage was found for the human generated text, but NLG resulted in decisions that were at least as good as those for the graphical displays with which staff were familiar. It is suggested that NLG might offer a viable automated approach to removing noise and artefacts in real, complex and dynamic data sets, thereby reducing visual complexity and mental workload, and enhancing decision-making particularly for inexperienced staff. Copyright © 2008 John Wiley & Sons, Ltd.
机译：向新生儿重症监护病房（NICU）的志愿者工作人员提供了匿名患者的生理数据集，这些数据是在大约45分钟的时间段内从以前的患者那里记录的。要求工作人员为显示数据的每个患者选择合适的医疗/护理措施。数据在以下三种情况之一中显示：（a）多线图，类似于病房中通常显示的线图，或（b）专业医疗/护理人员或（c）计算机自然语言生成（NLG）生成的文字描述。发现了人工生成文本的总体优势，但是NLG做出的决策至少与员工熟悉的图形显示的决策一样好。建议NLG提供一种可行的自动化方法，以消除真实，复杂和动态数据集中的噪声和伪像，从而降低视觉复杂性和脑力劳动量，并尤其是针对经验不足的员工增强决策能力。版权所有©2008 John Wiley＆Sons，Ltd.

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  • 来源
    《Applied Cognitive Psychology》 |2010年第1期|p.77-89|共13页
  • 作者

    Marian van der Meulen; Robert H. Logie; Yvonne Freer; Cindy Sykes; Neil McIntosh; Jim Hunter;

  • 作者单位

    Human Cognitive Neuroscience-Psychology, University of Edinburgh, UK;

    ||Human Cognitive Neuroscience-Psychology, University of Edinburgh, UK;

    Simpson Centre for Reproductive Health, Edinburgh Royal Infirmary, UK;

    Simpson Centre for Reproductive Health, Edinburgh Royal Infirmary, UK;

    Simpson Centre for Reproductive Health, Edinburgh Royal Infirmary, UK;

    Department of Computing Science, University of Aberdeen, UK;

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