Published on in Vol 21, No 7 (2019): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13719, first published .
A Real-Time Early Warning System for Monitoring Inpatient Mortality Risk: Prospective Study Using Electronic Medical Record Data

A Real-Time Early Warning System for Monitoring Inpatient Mortality Risk: Prospective Study Using Electronic Medical Record Data

A Real-Time Early Warning System for Monitoring Inpatient Mortality Risk: Prospective Study Using Electronic Medical Record Data

Chengyin Ye 1*, PhD;  Oliver Wang 2*, BA;  Modi Liu 2, BS;  Le Zheng 3, 4, PhD;  Minjie Xia 2, BS;  Shiying Hao 3, 4, PhD;  Bo Jin 2, MS;  Hua Jin 2, MS;  Chunqing Zhu 2, MS;  Chao Jung Huang 5, PhD;  Peng Gao 6, 7, PhD;  Gray Ellrodt 8, MD;  Denny Brennan 9, MEd, MBA;  Frank Stearns 2, MHA;  Karl G Sylvester 6, MD;  Eric Widen 2, MHA;  Doff B McElhinney 3, 4, MD;  Xuefeng Ling 3, 6, PhD

1 Department of Health Management, Hangzhou Normal University , Hangzhou , CN

2 HBI Solutions Inc , Palo Alto, CA, US

3 Clinical and Translational Research Program, Betty Irene Moore Children's Heart Center, Lucile Packard Children’s Hospital, Palo Alto, CA, US

4 Department of Cardiothoracic Surgery, Stanford University , Stanford, CA, US

5 National Taiwan University-Stanford Joint Program Office of AI in Biotechnology, Ministry of Science and Technology Joint Research Center for Artificial Intelligence Technology and All Vista Healthcare , Taipei , TW

6 Department of Surgery, Stanford University , Stanford, CA, US

7 Shandong University of Traditional Chinese Medicine , Shandong , CN

8 Department of Medicine, Berkshire Medical Center , Pittsfield, MA, US

9 Massachusetts Health Data Consortium , Waltham, CA, US

*these authors contributed equally

Corresponding Author:

  • Xuefeng Ling, PhD
  • Department of Surgery
  • Stanford University
  • S370 Grant Bldg, 300 Pasteur Drive
  • Stanford, CA
  • US
  • Phone: 1 6504279198
  • Email: bxling@stanford.edu