Correction: Analysis of healthcare service utilization after transport-related injuries by a mixture of hidden Markov models (PLoS ONE (2018) 13: 11 (e0206274) DOI: 10.1371/journal.pone.0206274)

Esmaili, N; Piccardi, M; Kruger, B; Girosi, F

Correction: Analysis of healthcare service utilization after transport-related injuries by a mixture of hidden Markov models (PLoS ONE (2018) 13: 11 (e0206274) DOI: 10.1371/journal.pone.0206274)

Esmaili, N Piccardi, M

Kruger, B Girosi, F

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Publication Type:: Journal Article
Citation:: PLoS ONE, 2019, 14 (4)
Issue Date:: 2019-04-01

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Field	Value	Language
dc.contributor.author	Esmaili, N	en_US
dc.contributor.author	Piccardi, M https://orcid.org/0000-0001-9250-6604	en_US
dc.contributor.author	Kruger, B	en_US
dc.contributor.author	Girosi, F	en_US
dc.date.issued	2019-04-01	en_US
dc.identifier.citation	PLoS ONE, 2019, 14 (4)	en_US
dc.identifier.uri	http://hdl.handle.net/10453/137041
dc.description.abstract	© 2019 Esmaili et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. After publication of this article [1], concerns were raised that the references to the software packages used for this analysis had been omitted. The authors utilized Stata Statistical Software: Release 15. The reference is StataCorp. 2017. Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC. The authors also utilized the seqHMM package package in R. The reference is: Helske S, Helske J. Mixture hidden markov models for sequence data: the seqhmm package in R. arXiv preprint arXiv:1704.00543. 2017 Apr 3.	en_US
dc.relation.ispartof	PLoS ONE	en_US
dc.relation.isbasedon	10.1371/journal.pone.0214973	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	General Science & Technology	en_US
dc.title	Correction: Analysis of healthcare service utilization after transport-related injuries by a mixture of hidden Markov models (PLoS ONE (2018) 13: 11 (e0206274) DOI: 10.1371/journal.pone.0206274)	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	14	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access	*
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	14	en_US

Abstract:

© 2019 Esmaili et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. After publication of this article [1], concerns were raised that the references to the software packages used for this analysis had been omitted. The authors utilized Stata Statistical Software: Release 15. The reference is StataCorp. 2017. Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC. The authors also utilized the seqHMM package package in R. The reference is: Helske S, Helske J. Mixture hidden markov models for sequence data: the seqhmm package in R. arXiv preprint arXiv:1704.00543. 2017 Apr 3.

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