A machine learning approach for predicting textbook outcome after cytoreductive surgery and hyperthermic intraperitoneal chemotherapy.
Amir Ashraf GanjoueiFernanda Romero-HernandezJaeyun Jane WangAhmed HamedAhmed AlaaDavid BartlettAdnan AlseidiMohammad Haroon ChoudryMohamed AdamPublished in: World journal of surgery (2024)
This is a data-driven study to predict the probability of achieving TO after CRS-HIPEC. The proposed pipeline has the potential to not only identify patients for whom surgery is not associated with prohibitive risk, but also aid surgeons in communicating this risk to patients.
Keyphrases
- end stage renal disease
- machine learning
- ejection fraction
- newly diagnosed
- chronic kidney disease
- minimally invasive
- peritoneal dialysis
- squamous cell carcinoma
- coronary artery bypass
- patient reported outcomes
- radiation therapy
- risk assessment
- coronary artery disease
- acute coronary syndrome
- percutaneous coronary intervention
- big data
- deep learning
- patient reported
- data analysis