Text Generation in Clinical Medicine – a Review

Hüske-Kraus, Dirk

doi:10.1055/s-0038-1634209

Cited by 29 publications

(18 citation statements)

References 50 publications

(31 reference statements)

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“…In the medical context, there has been a significant focus on summarisation from textual resources [1], but data-to-text technology has had a more restricted application [57] and existing systems have largely focussed on discrete data (e.g. [51,56,64]).…”

Section: Natural Language Generation and Data-to-text Technologymentioning

confidence: 99%

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

et al. 2009

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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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Section: Natural Language Generation and Data-to-text Technologymentioning

confidence: 99%

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

et al. 2009

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“…A number of medical informatics systems have been developed which use NLG technology; see the review by Hueske-Kraus [25]. Most of these are not data-to-text systems in the sense of automatically generating linguistic summaries of large clinical data sets; instead they focus on generating patient information material (often to support behaviour change) [26], helping clinicians write routine documents [27], summarising medical literature [28], and generating explanations for recommendations [29].…”

Section: Cardiovasc Ularmentioning

confidence: 99%

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

Hunter¹,

Freer²,

Gatt³

et al. 2012

Artificial Intelligence in Medicine

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“…Routine reports such as referral letters or patient examination findings are common in clinical practice. Current methods to produce these routine texts, such as the use of canned text or dictation, are far from optimal [37]. The Suregen system [38] used data-to-text to assist physicians in a hospital to write cardiology routine case reports.…”

Section: Routine Reporting In Clinical Practicementioning

confidence: 99%

Making Effective Use of Healthcare Data Using Data-to-Text Technology

Pauws

Gatt

Krahmer

et al. 2019

Data Science for Healthcare

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Healthcare organizations are in a continuous effort to improve health outcomes, reduce costs and enhance patient experience of care. Data is essential to measure and help achieving these improvements in healthcare delivery. Consequently, a data influx from various clinical, financial and operational sources is now overtaking healthcare organizations and their patients. The effective use of this data, however, is a major challenge. Clearly, text is an important medium to make data accessible. Financial reports are produced to assess healthcare organizations on some key performance indicators to steer their healthcare delivery. Similarly, at a clinical level, data on patient status is conveyed by means of textual descriptions to facilitate patient review, shift handover and care transitions. Likewise, patients are informed about data on their health status and treatments via text, in the form of reports or via ehealth platforms by their doctors. Unfortunately, such text is the outcome of a highly labour-intensive process if it is done by healthcare professionals. It is also prone to incompleteness, subjectivity and hard to scale up to different domains, wider audiences and varying communication purposes. Data-to-text is a recent breakthrough technology in artificial intelligence which automatically generates natural language in the form of text or speech from data. This chapter provides a survey of datato-text technology, with a focus on how it can be deployed in a healthcare setting. It will (1) give an up-to-date synthesis of data-to-text approaches, (2) give a categorized overview of use cases in healthcare, (3) seek to make a strong case for evaluating and implementing data-to-text in a healthcare setting, and (4) highlight recent research challenges.

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Text Generation in Clinical Medicine – a Review

Cited by 29 publications

References 50 publications

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

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

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

Making Effective Use of Healthcare Data Using Data-to-Text Technology

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