A reinforcement learning-based optimal control approach for managing an elective surgery backlog after pandemic disruption.

Contagious disease pandemics, such as COVID-19, can cause hospitals around the world to delay nonemergent elective surgeries, which results in a large surgery backlog. To develop an operational solution for providing patients timely surgical care with limited health care resources, this study proposes a stochastic control process-based method that helps hospitals make operational recovery plans to clear their surgery backlog and restore surgical activity safely. The elective surgery backlog recovery process is modeled by a general discrete-time queueing network system, which is formulated by a Markov decision process.

Machine learning approaches for the prediction of postoperative complication risk in liver resection patients.

Background: For liver cancer patients, the occurrence of postoperative complications increases the difficulty of perioperative nursing, prolongs the hospitalization time of patients, and leads to large increases in hospitalization costs. The ability to identify influencing factors and to predict the risk of complications in patients with liver cancer after surgery could assist doctors to make better clinical decisions.

Objective: The aim of the study was to develop a postoperative complication risk prediction model based on machine learning algorithms, which utilizes variables obtained before or during the liver cancer surgery, to predict when complications present with clinical symptoms and the ways of reducing the risk of complications.

Cost Control of Treatment for Cerebrovascular Patients Using a Machine Learning Model in Western China.

Background: Cerebrovascular disease has been the leading cause of death in China since 2017, and the control of medical expenses for these diseases is an urgent issue. Diagnosis-related groups (DRG) are increasingly being used to decrease the costs of healthcare worldwide. However, the classification variables and rules used vary from region to region.

Risk factors associated with postoperative complications after liver cancer resection surgery in western China.

Objectives: Postoperative complications increase the workload of nursing staff as well as the financial and mental distress suffered by patients. The objective of this study is to identify clinical factors associated with postoperative complications after liver cancer resection surgery.

Methods: Data from liver cancer resections occurring between January 1st, 2019 to December 31st, 2019 was collected from the Department of Liver Surgery in West China Hospital of Sichuan University.