TY - JOUR T1 - Temporal prediction of in-hospital falls using tensor factorisation JF - BMJ Innovations JO - BMJ Innov DO - 10.1136/bmjinnov-2017-000221 SP - bmjinnov-2017-000221 AU - Haolin Wang AU - Qingpeng Zhang AU - Hing-Yu So AU - Angela Kwok AU - Zoie Shui-Yee Wong Y1 - 2018/03/09 UR - http://innovations.bmj.com/content/early/2018/03/09/bmjinnov-2017-000221.abstract N2 - 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. ER -