Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm.

Journal: International journal of medical informatics
Published Date: May 1, 2020

Abstract

OBJECTIVE: Predicting the risk of falls in advance can benefit the quality of care and potentially reduce mortality and morbidity in the older population. The aim of this study was to construct and validate an electronic health record-based fall risk predictive tool to identify elders at a higher risk of falls.

Authors

  • Chengyin Ye
    Department of Health Management, Hangzhou Normal University, Hangzhou, China.
  • Jinmei Li
    Department of Health Management, Hangzhou Normal University, Hangzhou, China. Electronic address: lijinmei@stu.hznu.edu.cn.
  • Shiying Hao
    Departments of Surgery, Stanford University, Stanford, CA 94305, USA.
  • Modi Liu
    HBI Solutions Inc, Palo Alto, CA, United States.
  • Hua Jin
    HBI Solutions Inc, Palo Alto, CA, United States.
  • Le Zheng
    Departments of Surgery, Stanford University, Stanford, CA 94305, USA.
  • Minjie Xia
    HBI Solutions Inc, Palo Alto, CA, United States.
  • Bo Jin
    HBISolutions Inc., Palo Alto, CA 94301, USA.
  • Chunqing Zhu
    HBISolutions Inc., Palo Alto, CA 94301, USA.
  • Shaun T Alfreds
    HealthInfoNet, Portland, ME 04103, USA.
  • Frank Stearns
    HBISolutions Inc., Palo Alto, CA 94301, USA.
  • Laura Kanov
    HBI Solutions Inc, Palo Alto, CA, USA.
  • Karl G Sylvester
    Departments of Surgery, Stanford University, Stanford, CA 94305, USA.
  • Eric Widen
    HBISolutions Inc., Palo Alto, CA 94301, USA.
  • Doff McElhinney
    Department of Cardiothoracic Surgery, Stanford University, Stanford, CA, United States.
  • Xuefeng Bruce Ling
    Clinical and Translational Research Program, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Palo Alto, CA, United States; Department of Surgery, Stanford University, Stanford, CA, United States. Electronic address: bxling@stanford.edu.

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