Prediction of individual patient readmissions for elective carotid endarterectomy, aortofemoral bypass/aortic aneurysm repair, and femoral-distal arterial bypass
R version was the current version at the time, R 3.5.
This scientific paper is about using machine learning to predict whether patients who have had certain types of surgeries will need to be readmitted to the hospital within 90 days of their initial discharge. The surgeries in question are elective carotid endarterectomy, aortofemoral bypass/aortic aneurysm repair, and femoral-distal arterial bypass. The researchers used data from the Healthcare Cost and Utilization Project State Inpatient Database for Florida State to develop a predictive model. They looked at various factors that might influence whether a patient would need to be readmitted, such as age, gender, race, income level, type of insurance, length of stay in the hospital, and other medical conditions the patient might have. They used a machine learning algorithm to analyze this data and make predictions about which patients were most likely to be readmitted. The goal was to help doctors, and hospitals identify patients who might need extra care or follow-up after their surgery in order to prevent readmissions. Overall, this study shows how machine learning can be used in healthcare to improve patient outcomes and reduce costs by predicting which patients are at the highest risk for readmission after surgery.