Published on in Vol 11 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/41576, first published .
Using the H2O Automatic Machine Learning Algorithms to Identify Predictors of Web-Based Medical Record Nonuse Among Patients in a Data-Rich Environment: Mixed Methods Study

Using the H2O Automatic Machine Learning Algorithms to Identify Predictors of Web-Based Medical Record Nonuse Among Patients in a Data-Rich Environment: Mixed Methods Study

Using the H2O Automatic Machine Learning Algorithms to Identify Predictors of Web-Based Medical Record Nonuse Among Patients in a Data-Rich Environment: Mixed Methods Study

Yang Chen   1 , BSc ;   Xuejiao Liu   1 , BEng ;   Lei Gao   2 , LLM ;   Miao Zhu   3 , PhD ;   Ben-Chang Shia   4, 5 , PhD ;   Mingchih Chen   4, 5 , PhD ;   Linglong Ye   6 , PhD ;   Lei Qin   1, 7 , PhD

1 School of Statistics, University of International Business and Economics, Beijing, China

2 School of Law, University of International Business and Economics, Beijing, China

3 School of Statistics, Huaqiao University, Xiamen, China

4 Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, New Taipei City, Taiwan

5 Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City, Taiwan

6 School of Public Affairs, Xiamen University, Xiamen, China

7 Dong Fureng Institute of Economic and Social Development, Wuhan University, Wuhan, China

Corresponding Author:

  • Lei Qin, PhD
  • School of Statistics, University of International Business and Economics
  • No.10, Huixin Dongjie, Chaoyang District
  • Beijing, 100029
  • China
  • Phone: 86 01064491146
  • Email: qinlei@uibe.edu.cn