Applying contrastive pre-training for depression and anxiety risk prediction in type 2 diabetes patients based on heterogeneous electronic health records: a primary healthcare case study.
Wei FengHonghan WuHui MaZhenhuan TaoMengdie XuXin ZhangShan LuCheng WanYun LiuPublished in: Journal of the American Medical Informatics Association : JAMIA (2023)
The DAP model effectively predicted post-discharge depression and anxiety in T2DM patients from PHS, reducing data fragmentation and limitations. This study highlights the DAP model's potential for early detection and intervention in depression and anxiety, improving outcomes for diabetes patients.
Keyphrases
- type diabetes
- end stage renal disease
- healthcare
- electronic health record
- newly diagnosed
- ejection fraction
- chronic kidney disease
- randomized controlled trial
- cardiovascular disease
- prognostic factors
- glycemic control
- insulin resistance
- risk assessment
- metabolic syndrome
- big data
- deep learning
- skeletal muscle
- adverse drug