Exploring hidden in-hospital fall clusters from incident reports using text analytics

Jiaxing Liu; Zoie Shui-Yee Wong; Kwok-Leung Tsui; Hing-Yu So; Angela Kwok

doi:10.3233/SHTI190517

Exploring hidden in-hospital fall clusters from incident reports using text analytics

Jiaxing Liu
, Zoie Shui-Yee Wong^*
, Kwok-Leung Tsui
, Hing-Yu So
, Angela Kwok

^*Corresponding author for this work

Department of Data Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

6 Citations (Scopus)

Abstract

Retrospective analysing of fall incident reports can uncover hidden information, identify potential risk factors, and improve healthcare quality. This study explores potential fall incident clusters using word embeddings and hierarchical clustering. Fall incident reports from 7 local hospitals in Hong Kong were catalogued into 5 potential clusters with significantly different fall severity, gender, reporting department, and keywords. This study demonstrates the feasibility of using text clustering methods on real-world fall incident reports mining.

Original language	English
Title of host publication	MEDINFO 2019
Subtitle of host publication	Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics
Editors	Lucila Ohno-Machado , Brigitte Séroussi
Publisher	IOS Press
Pages	1526-1527
ISBN (Electronic)	978-1-64368-003-3
ISBN (Print)	978-1-64368-002-6
DOIs	https://doi.org/10.3233/SHTI190517
Publication status	Published - Aug 2019
Event	17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, France Duration: 25 Aug 2019 → 30 Aug 2019

Publication series

Name	Studies in Health Technology and Informatics
Volume	264
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	17th World Congress on Medical and Health Informatics, MEDINFO 2019
Place	France
City	Lyon
Period	25/08/19 → 30/08/19

Research Keywords

Natural language processing
Patient safety
Unsupervised machine learning

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10.3233/SHTI190517

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Liu, J., Wong, Z. S.-Y., Tsui, K.-L., So, H.-Y., & Kwok, A. (2019). Exploring hidden in-hospital fall clusters from incident reports using text analytics. In L. Ohno-Machado , & B. Séroussi (Eds.), MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics (pp. 1526-1527). (Studies in Health Technology and Informatics; Vol. 264). IOS Press. https://doi.org/10.3233/SHTI190517

Liu, Jiaxing ; Wong, Zoie Shui-Yee ; Tsui, Kwok-Leung et al. / Exploring hidden in-hospital fall clusters from incident reports using text analytics. MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. editor / Lucila Ohno-Machado ; Brigitte Séroussi . IOS Press, 2019. pp. 1526-1527 (Studies in Health Technology and Informatics).

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title = "Exploring hidden in-hospital fall clusters from incident reports using text analytics",

abstract = "Retrospective analysing of fall incident reports can uncover hidden information, identify potential risk factors, and improve healthcare quality. This study explores potential fall incident clusters using word embeddings and hierarchical clustering. Fall incident reports from 7 local hospitals in Hong Kong were catalogued into 5 potential clusters with significantly different fall severity, gender, reporting department, and keywords. This study demonstrates the feasibility of using text clustering methods on real-world fall incident reports mining.",

keywords = "Natural language processing, Patient safety, Unsupervised machine learning",

author = "Jiaxing Liu and Wong, \{Zoie Shui-Yee\} and Kwok-Leung Tsui and Hing-Yu So and Angela Kwok",

year = "2019",

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Liu, J, Wong, ZS-Y, Tsui, K-L, So, H-Y & Kwok, A 2019, Exploring hidden in-hospital fall clusters from incident reports using text analytics. in L Ohno-Machado & B Séroussi (eds), MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. Studies in Health Technology and Informatics, vol. 264, IOS Press, pp. 1526-1527, 17th World Congress on Medical and Health Informatics, MEDINFO 2019, Lyon, France, 25/08/19. https://doi.org/10.3233/SHTI190517

Exploring hidden in-hospital fall clusters from incident reports using text analytics. / Liu, Jiaxing; Wong, Zoie Shui-Yee; Tsui, Kwok-Leung et al.
MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. ed. / Lucila Ohno-Machado ; Brigitte Séroussi . IOS Press, 2019. p. 1526-1527 (Studies in Health Technology and Informatics; Vol. 264).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Exploring hidden in-hospital fall clusters from incident reports using text analytics

AU - Liu, Jiaxing

AU - Wong, Zoie Shui-Yee

AU - Tsui, Kwok-Leung

AU - So, Hing-Yu

AU - Kwok, Angela

PY - 2019/8

Y1 - 2019/8

N2 - Retrospective analysing of fall incident reports can uncover hidden information, identify potential risk factors, and improve healthcare quality. This study explores potential fall incident clusters using word embeddings and hierarchical clustering. Fall incident reports from 7 local hospitals in Hong Kong were catalogued into 5 potential clusters with significantly different fall severity, gender, reporting department, and keywords. This study demonstrates the feasibility of using text clustering methods on real-world fall incident reports mining.

AB - Retrospective analysing of fall incident reports can uncover hidden information, identify potential risk factors, and improve healthcare quality. This study explores potential fall incident clusters using word embeddings and hierarchical clustering. Fall incident reports from 7 local hospitals in Hong Kong were catalogued into 5 potential clusters with significantly different fall severity, gender, reporting department, and keywords. This study demonstrates the feasibility of using text clustering methods on real-world fall incident reports mining.

KW - Natural language processing

KW - Patient safety

KW - Unsupervised machine learning

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85071511274&origin=recordpage

U2 - 10.3233/SHTI190517

DO - 10.3233/SHTI190517

M3 - RGC 32 - Refereed conference paper (with host publication)

C2 - 31438214

SN - 978-1-64368-002-6

T3 - Studies in Health Technology and Informatics

SP - 1526

EP - 1527

BT - MEDINFO 2019

A2 - Ohno-Machado , Lucila

A2 - Séroussi , Brigitte

PB - IOS Press

T2 - 17th World Congress on Medical and Health Informatics, MEDINFO 2019

Y2 - 25 August 2019 through 30 August 2019

ER -

Liu J, Wong ZSY, Tsui KL, So HY, Kwok A. Exploring hidden in-hospital fall clusters from incident reports using text analytics. In Ohno-Machado L, Séroussi B, editors, MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. IOS Press. 2019. p. 1526-1527. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI190517

Exploring hidden in-hospital fall clusters from incident reports using text analytics

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