Prediction of prognosis in elderly patients with sepsis based on machine learning (random survival forest).
Luming ZhangTao HuangFengshuo XuShaojin LiShuai ZhengJun LyuHaiyan YinPublished in: BMC emergency medicine (2022)
We constructed a prognostic model to predict a 30-day mortality risk in elderly patients with sepsis based on machine learning (RSF algorithm), and it proved superior to the traditional scoring systems. The risk factors affecting the patients were also ranked. In addition to the common risk factors of vasopressors, ventilator use, and urine output. Newly added factors such as RDW, type of ICU unit, malignant cancer, and metastatic solid tumor also significantly influence prognosis.
Keyphrases
- machine learning
- intensive care unit
- risk factors
- end stage renal disease
- acute kidney injury
- artificial intelligence
- ejection fraction
- newly diagnosed
- chronic kidney disease
- septic shock
- mechanical ventilation
- small cell lung cancer
- big data
- climate change
- peritoneal dialysis
- prognostic factors
- papillary thyroid
- wastewater treatment