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Multimodal predictor of neurodevelopmental outcome in newborns with hypoxic-ischaemic encephalopathy

Overview of attention for article published in Computers in Biology & Medicine, August 2015
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Title
Multimodal predictor of neurodevelopmental outcome in newborns with hypoxic-ischaemic encephalopathy
Published in
Computers in Biology & Medicine, August 2015
DOI 10.1016/j.compbiomed.2015.05.017
Pubmed ID
Authors

Andriy Temko, Orla Doyle, Deirdre Murray, Gordon Lightbody, Geraldine Boylan, William Marnane

Abstract

Automated multimodal prediction of outcome in newborns with hypoxic-ischaemic encephalopathy is investigated in this work. Routine clinical measures and 1h EEG and ECG recordings 24h after birth were obtained from 38 newborns with different grades of HIE. Each newborn was reassessed at 24 months to establish their neurodevelopmental outcome. A set of multimodal features is extracted from the clinical, heart rate and EEG measures and is fed into a support vector machine classifier. The performance is reported with the statistically most unbiased leave-one-patient-out performance assessment routine. A subset of informative features, whose rankings are consistent across all patients, is identified. The best performance is obtained using a subset of 9 EEG, 2h and 1 clinical feature, leading to an area under the ROC curve of 87% and accuracy of 84% which compares favourably to the EEG-based clinical outcome prediction, previously reported on the same data. The work presents a promising step towards the use of multimodal data in building an objective decision support tool for clinical prediction of neurodevelopmental outcome in newborns with hypoxic-ischaemic encephalopathy.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 2%
Canada 1 2%
Unknown 50 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 19%
Student > Ph. D. Student 7 13%
Student > Master 7 13%
Student > Bachelor 7 13%
Other 6 12%
Other 9 17%
Unknown 6 12%
Readers by discipline Count As %
Medicine and Dentistry 19 37%
Engineering 8 15%
Computer Science 5 10%
Nursing and Health Professions 3 6%
Agricultural and Biological Sciences 1 2%
Other 6 12%
Unknown 10 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 May 2016.
All research outputs
#10,851,092
of 12,242,817 outputs
Outputs from Computers in Biology & Medicine
#600
of 715 outputs
Outputs of similar age
#228,727
of 276,082 outputs
Outputs of similar age from Computers in Biology & Medicine
#18
of 18 outputs
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So far Altmetric has tracked 715 research outputs from this source. They receive a mean Attention Score of 2.8. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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