BACKGROUND: Pulmonary tuberculosis (PTB) is a prevalent chronic disease associated with a significant economic burden on patients. Using machine learning to predict hospitalization costs can allocate medical resources effectively and optimize the cos...
BACKGROUND: With the steady rise in health care expenditures, the examination of factors that may influence the costs of care has garnered much attention. Although machine learning models have previously been applied in health economics, their applic...
Female pelvic medicine & reconstructive surgery
Feb 1, 2022
OBJECTIVES: Despite increasing use of robotic technology for minimally invasive hysterectomy with sacrocolpopexy, evidence supporting the benefits of these costly procedures remains inconclusive. This study aimed to compare differences in perioperati...
The introduction of robotic surgery in hospitals has raised much debate given the various effects on care, costs, education and medical advancement. Purchasing discussions are often approached with more questions than answers and there is a need for ...
BACKGROUND: Robot-assisted pancreatoduodenectomy (RPD) has shown some advantages over open pancreatoduodenectomy (OPD) but few studies have reported a cost analysis between the two techniques. We conducted a structured cost-analysis comparing pancrea...
HYPOTHESIS/PURPOSE: The objective is to develop and validate an artificial intelligence model, specifically an artificial neural network (ANN), to predict length of stay (LOS), discharge disposition, and inpatient charges for primary anatomic total (...
Hospital traditional cost accounting systems have inherent limitations that restrict their usefulness for measuring the exact cost of healthcare services. In this regard, new approaches such as Time Driven-Activity based Costing (TDABC) provide appro...
OBJECTIVE: To estimate costs attributable to robot-assisted laparoscopic prostatectomy (RALP) as compared with open prostatectomy (OP) and laparoscopic prostatectomies (LP) in a National Health Service perspective.
BACKGROUND AND OBJECTIVES: Kernel deep stacking networks (KDSNs) are a novel method for supervised learning in biomedical research. Belonging to the class of deep learning techniques, KDSNs are based on artificial neural network architectures that in...
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