Machine Learning Model for Risk Prediction of Community-Acquired Acute Kidney Injury Hospitalization From Electronic Health Records: Development and Validation Study.
Chien-Ning HsuChien-Liang LiuYou-Lin TainChin-Yu KuoYun-Chun LinPublished in: Journal of medical Internet research (2020)
A machine learning-generated risk score model can identify patients at risk of developing CA-AKI-related hospitalization through a routine care data-driven approach. The validated multivariate risk assessment tool could help clinicians to stratify patients in primary care, and to provide monitoring and early intervention for preventing AKI while improving the quality of AKI care in the general population.
Keyphrases
- acute kidney injury
- machine learning
- electronic health record
- cardiac surgery
- primary care
- healthcare
- palliative care
- end stage renal disease
- quality improvement
- randomized controlled trial
- ejection fraction
- newly diagnosed
- artificial intelligence
- chronic kidney disease
- peritoneal dialysis
- big data
- mental health
- deep learning
- pain management
- adverse drug