A risk prediction model based on machine learning for postoperative cognitive dysfunction in elderly patients with non-cardiac surgery.
Xianhai XieJunlin LiYi ZhongZhaojing FangYue FengChen ChenXiaoMing DaiYanna SiPublished in: Aging clinical and experimental research (2023)
Based on six important perioperative variables, we successfully established a series of ML models for predicting POCD occurrence at 3 months after surgery in elderly non-cardiac patients, with SVM model being the best-performing model. Our models are expected to serve as decision aids for clinicians to monitor screened high-risk patients more closely or to consider further interventions.
Keyphrases
- cardiac surgery
- machine learning
- end stage renal disease
- patients undergoing
- acute kidney injury
- newly diagnosed
- chronic kidney disease
- ejection fraction
- risk assessment
- physical activity
- left ventricular
- middle aged
- palliative care
- peritoneal dialysis
- atrial fibrillation
- patient reported outcomes
- antiretroviral therapy
- patient reported