Artificial Intelligence Prediction Model for the Cost and Mortality of Renal Replacement Therapy in Aged and Super-Aged Populations in Taiwan.
Shih-Yi LinMeng-Hsuen HsiehCheng-Li LinMeng-Ju HsiehWu-Huei HsuCheng-Chieh LinChung Y HsuChia-Hung KaoPublished in: Journal of clinical medicine (2019)
Applying artificial intelligence modeling could help to provide reliable information about one-year outcomes following dialysis in the aged and super-aged populations; those with cancer, alcohol-related disease, stroke, chronic obstructive pulmonary disease (COPD), previous hip fracture, osteoporosis, dementia, and previous respiratory failure had higher medical costs and a high mortality rate.
Keyphrases
- artificial intelligence
- chronic obstructive pulmonary disease
- machine learning
- big data
- deep learning
- hip fracture
- respiratory failure
- cardiovascular events
- lung function
- healthcare
- acute kidney injury
- extracorporeal membrane oxygenation
- risk factors
- mechanical ventilation
- atrial fibrillation
- mild cognitive impairment
- intensive care unit
- cardiovascular disease
- cystic fibrosis
- body composition
- lymph node metastasis
- alcohol consumption
- peritoneal dialysis