TY  - JOUR
PY  - 2019//
TI  - Exploring hidden in-hospital fall clusters from incident reports using text analytics
JO  - Studies in health technology and informatics
A1  - Liu, Jiaxing
A1  - Wong, Zoie Shui-Yee
A1  - Tsui, Kwok-Leung
A1  - So, Hing-Yu
A1  - Kwok, Angela
SP  - 1526
EP  - 1527
VL  - 264
IS  - 
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.<p />  <p>Language: en</p>
LA  - en
SN  - 0926-9630
UR  - http://dx.doi.org/10.3233/SHTI190517
ID  - ref1
ER  -