An Intelligent Patient Admission Model of Day Surgery Using Heterogeneous Data with Semi-Supervised Learning

In recent years, day surgery has established a strong reputation and popularity. It has a number of advantages over traditional surgery, including a shorter hospital stay, a lower risk of hospital-associated infections, and a higher cost efficiency. However, the patient admission criteria for day surgery were primarily dependent on manual guidelines defined by specialists, which lacked data driving from the real world and potentially wasted a lot of medical resources. In this paper, we proposed a day surgery patient admission algorithm, which is built by semi-supervised learning that combines both structured data and unstructured diagnoses to help surgeons make speedy admission decisions. We test this algorithm with the clinical data of day surgery patients who underwent laparoscopic cholecystectomy at West China Hospital and achieve an accuracy of 0.85 and an f1-score of 0.83, as well as reaching 0.97 on the precision score. The result is potentially broadly applicable to more day surgery types.