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 ChenXuejiao LiuLei GaoMiao ZhuBen-Chang ShiaMing-Chih ChenLinglong YeLei QinPublished in: JMIR medical informatics (2023)
When monitoring web-based medical record use trends, research should focus on social factors such as age, education, BMI, and marital status, as well as personal lifestyle and behavioral habits, including smoking, use of electronic devices and the internet, patients' personal health status, and their level of health concern. The use of electronic medical records can be targeted to specific patient groups, allowing more people to benefit from their usefulness.
Keyphrases
- machine learning
- healthcare
- end stage renal disease
- deep learning
- mental health
- big data
- ejection fraction
- public health
- chronic kidney disease
- cardiovascular disease
- health information
- newly diagnosed
- artificial intelligence
- peritoneal dialysis
- metabolic syndrome
- case report
- prognostic factors
- randomized controlled trial
- cancer therapy
- electronic health record
- weight loss
- study protocol
- weight gain
- smoking cessation