Renal cell carcinoma is a common malignancy of the urinary system. Surgery can save renal cell carcinoma patients’ lives, but concerns associated with their quality of life are a major clinical concern. Herein, we have analyzed the value of fatigue, resistance, aerobic, illness, and lost nursing, commonly known as FRAIL nursing, on the improvement in rehabilitation quality and nutritional status of renal cell carcinoma patients. The results show that compared with renal cell carcinoma patients in the control group receiving routine care, patients who received fatigue, resistance, aerobic, illness, and lost nursing had significantly shortened postoperative rehabilitation time, reduced incidence of postoperative complications, and enhanced nursing compliance and quality of life. Also, the nutritional status and immune function of renal cell carcinoma patients were significantly improved after fatigue, resistance, aerobic, illness, and lost nursing, demonstrating its high application value in clinical treatment of renal cell carcinoma.

Hospitals' daily operations have become increasingly dependent on medical devices. However, the occurrence of faults is inevitable. Therefore, it is crucial for hospitals to make timely fault diagnoses and enact the corresponding measures and improvements. This paper proposes a novel concept lattice method for the intelligent diagnosis of medical device faults. To minimize the influence of uncertain factors, fuzzy sets are used to accurately express relationships between concepts. First, the occurrence frequency and severity of each fault type are extracted based on the collected information. Then, the fuzzy formal context of occurrent faults and known faults can be constructed. Next, the corresponding fuzzy concept lattice is established and visualized using a Hasse diagram. Finally, the similarity between the concept lattices is calculated and used for fault diagnosis. Here, the weight factors are determined using the decision-making trial and evaluation laboratory (DEMATEL) method. A comparative analysis is performed to show that the proposed method uses simple calculations and is highly accurate.

Rh-negative rare blood inventory protection plays an important role in emergency blood protection. Normally, hospitals typically hold a fixed amount of daily reserve in response to emergency needs, but the measure can increase the unnecessary cost of repeated freezing and thawing. In order to save manpower, protect blood resources and reduce costs, a two-stage stochastic model is proposed to determine the optimal daily reserve of Rh-negative red blood cells, taking into account the uncertainty of demand. First, the model focuses on minimizing operational cost, shortage cost and damage caused by blood substitution. Then, the proposed model generates a series of discrete scenarios to solve the uncertainty of demand and predict the demand. In addition, a case study is presented to prove the validity of the proposed model with real data. Sensitivity analysis is also established to observe the effect of parameter changes on the results. Finally, the results show that the proposed model can effectively reduce the cost and current waste.