PT  - JOURNAL ARTICLE
AU  - Haolin Wang
AU  - Qingpeng Zhang
AU  - Hing-Yu So
AU  - Angela Kwok
AU  - Zoie Shui-Yee Wong
TI  - Temporal prediction of in-hospital falls using tensor factorisation
AID  - 10.1136/bmjinnov-2017-000221
DP  - 2018 Mar 09
TA  - BMJ Innovations
PG  - bmjinnov-2017-000221
4099  - http://innovations.bmj.com/content/early/2018/03/09/bmjinnov-2017-000221.short
4100  - http://innovations.bmj.com/content/early/2018/03/09/bmjinnov-2017-000221.full
AB  - 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.