Published on in Vol 8, No 6 (2020): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18186, first published .
Artificial Intelligence–Based Multimodal Risk Assessment Model for Surgical Site Infection (AMRAMS): Development and Validation Study

Artificial Intelligence–Based Multimodal Risk Assessment Model for Surgical Site Infection (AMRAMS): Development and Validation Study

Artificial Intelligence–Based Multimodal Risk Assessment Model for Surgical Site Infection (AMRAMS): Development and Validation Study

Weijia Chen   1 * , MD ;   Zhijun Lu   1 * , MD, PhD ;   Lijue You   2 , MS ;   Lingling Zhou   3 , BSc ;   Jie Xu   4, 5 , MPhil ;   Ken Chen   1, 5, 6 , MD, DHM

1 Department of Anesthesiology, Rui Jin Hospital, Luwan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China

2 Department of Informatics, Rui Jin Hospital, Luwan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China

3 Department of Infection Prevention and Control, Rui Jin Hospital, Luwan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China

4 VitalStrategic Research Institute, Shanghai, China

5 Synyi Research, Shanghai, China

6 Precision Diagnosis and Image Guided Therapy, Philips Research China, Shanghai, China

*these authors contributed equally

Corresponding Author:

  • Ken Chen, MD, DHM
  • Department of Anesthesiology
  • Rui Jin Hospital, Luwan Branch
  • Shanghai Jiao Tong University School of Medicine
  • South Chongqing Road, No 149
  • Shanghai
  • China
  • Phone: 86 021-63864050
  • Email: nutastray@gmail.com