Risk factors and risk assessment tools for falls in hospital in-patients: a systematic review.

By D. Oliver, F. Daly, F. Martin, M. Mcmurdo. Age and Ageing, 2004

719 citations recorded by the source corpus.

Subfields: Critical Care and Intensive Care Medicine, Geriatrics and Gerontology, Physical Therapy, Sports Therapy and Rehabilitation

View Paper: https://www.semanticscholar.org/paper/fab7b313293227e67c3a1108c824f01efa23656c

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yixue Hao
2. Kai Hwang
3. Lu Wang
4. Lin Wang
5. Min Chen
6. Yujie Li

Citations in the corpus

• Disease Prediction by Machine Learning Over Big Data From Healthcare Communities (2017, background): “These models are valuable in clinical situations and arewidely studied [15], [16].”
• Is the Timed Up and Go test a useful predictor of risk of falls in community dwelling older adults: a systematic review and meta- analysis (2014, neutral): “These tools serve to identify patients at risk of falling however, the sensitivity and specificity of existing tools is low [5].”