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Yvonne Freer
Researcher at Edinburgh Royal Infirmary
Publications - 30
Citations - 1181
Yvonne Freer is an academic researcher from Edinburgh Royal Infirmary. The author has contributed to research in topics: Intensive care & Decision support system. The author has an hindex of 15, co-authored 30 publications receiving 1096 citations. Previous affiliations of Yvonne Freer include Scottish National Blood Transfusion Service & University of Edinburgh.
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Journal ArticleDOI
Automatic generation of textual summaries from neonatal intensive care data
François Portet,Ehud Reiter,Albert Gatt,Jim Hunter,Somayajulu Sripada,Yvonne Freer,Cindy Sykes +6 more
TL;DR: A prototype, called BT-45, is presented, which generates textual summaries of about 45 minutes of continuous physiological signals and discrete events and brings together techniques from the different areas of signal processing, medical reasoning, knowledge engineering, and natural language generation.
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A comparison of graphical and textual presentations of time series data to support medical decision making in the neonatal intensive care unit.
TL;DR: In this experimental task, participants performed better when presented with a textual summary of the medical scenario than when it was presented as a set of trend graphs, which could be a useful feature of decision support tools in the intensive care unit.
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Prematurity and neonatal noxious events exert lasting effects on infant pain behaviour.
TL;DR: Either PT birth or repetitive procedures associated with such birth alters the sensitivity threshold of PT infants compared with FT infants for at least the first year of life.
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Sucrose and non-nutritive sucking for the relief of pain in screening for retinopathy of prematurity: a randomised controlled trial
Elaine M. Boyle,Yvonne Freer,Zareena Khan-Orakzai,Michael Watkinson,Elizabeth Wright,John R Ainsworth,Neil McIntosh +6 more
TL;DR: Non-nutritive sucking reduced distress responses in infants undergoing screening for retinopathy of prematurity, and the difference in response was large enough to be detected by a validated assessment tool.
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Autoregressive Hidden Markov Models for the Early Detection of Neonatal Sepsis
TL;DR: This paper investigates the extent to which physiological events observed in the patient's monitoring traces could be used for the early detection of neonatal sepsis and model the distribution of these events with an autoregressive hidden Markov model (AR-HMM).