Natural Language Processing in a Clinical Decision Support System for the Identification of Venous Thromboembolism: Algorithm Development and Validation.
Zhi-Geng JinHui ZhangMei-Hui TaiYing YangYuan YaoYu-Tao GuoPublished in: Journal of medical Internet research (2023)
The NLP algorithm in our DeVTEcare identified VTE well across different clinical settings, especially in patients in surgery units, departments with low-risk VTE, and patients aged ≤65 years. This algorithm can help to inform accurate in-hospital VTE rates and enhance risk-classified VTE integrated care in future research.
Keyphrases
- venous thromboembolism
- end stage renal disease
- direct oral anticoagulants
- ejection fraction
- clinical decision support
- machine learning
- newly diagnosed
- healthcare
- prognostic factors
- minimally invasive
- emergency department
- peritoneal dialysis
- deep learning
- palliative care
- atrial fibrillation
- high resolution
- coronary artery bypass
- patient reported outcomes
- pain management
- acute coronary syndrome