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Health IT, systems and process innovations
Original article
Temporal prediction of in-hospital falls using tensor factorisation
  1. Haolin Wang1,2,3,
  2. Qingpeng Zhang2,4,
  3. Hing-Yu So5,
  4. Angela Kwok5,
  5. Zoie Shui-Yee Wong6
  1. 1 Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
  2. 2 Department of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong, China
  3. 3 University of Chinese Academy of Sciences, Beijing, China
  4. 4 Shenzhen Research Institute, City University of Hong Kong, Shenzhen, Guangdong, China
  5. 5 New Territories East Cluster (NTEC), Quality and Safety Division Prince of Wales Hospital, New Territories, Hong Kong, China
  6. 6 Graduate School of Public Health, St. Luke’s International University, Tokyo, Japan
  1. Correspondence to Dr Qingpeng Zhang, Department of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong, China ; qingpeng.zhang{at}cityu.edu.hk

Abstract

In-hospital fall incidence is a critical indicator of healthcare outcome. Predictive models for fall incidents could facilitate optimal resource planning and allocation for healthcare providers. In this paper, we proposed a tensor factorisation-based framework to capture the latent features for fall incidents prediction over time. Experiments with real-world data from local hospitals in Hong Kong demonstrated that the proposed method could predict the fall incidents reasonably well (with an area under the curve score around 0.9). As compared with the baseline time series models, the proposed tensor based models were able to successfully identify high-risk locations without records of fall incidents during the past few months.

  • digital health
  • data mining
  • fall prevention
  • inventions
  • machine learning

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

https://doi.org/10.1136/bmjinnov-2017-000221

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    Footnotes

    • Contributors HW and QZ designed the model and performed the experiments. ZS-YW, AK and H-YS collected the data. HW and ZS-YW performed data analysis. All authors contributed to the writing of the paper.

    • Funding HW and QZ are supported by the National Natural Science Foundation of China (NSFC) under grants 71402157 and 71672163 and in part by the Theme-Based Research Scheme of the Research Grants Council of Hong Kong under Grant T32-102/14N.

    • Competing interests None declared.

    • Patient consent Not required.

    • Ethics approval Research committee of City University of Hong Kong.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data sharing statement The raw hospital data are prohibited to share. The authors are happy to share the codes and hypothetical dummy data used in this research. Please email qingpeng.zhang@cityu.edu.hk.