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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
Author(s) -
van der Meulen Marian,
Logie Robert H.,
Freer Yvonne,
Sykes Cindy,
McIntosh Neil,
Hunter Jim
Publication year - 2010
Publication title -
applied cognitive psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.719
H-Index - 100
eISSN - 1099-0720
pISSN - 0888-4080
DOI - 10.1002/acp.1545
Subject(s) - natural language generation , workload , computer science , psychology , human–computer interaction , graph , artificial intelligence , natural language , nursing , natural language processing , medicine , theoretical computer science , operating system
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 medical/nursing 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 medical/nursing 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.