Evaluation and optimization of supply factor efficiency for public digital cultural services in China

Purpose

This investigation is designed to quantify and appraise the efficiency of resource distribution in the provision of public digital cultural services in China. By acknowledging and incorporating the realities of China's social development, the authors offer recommendations for enhancement derived from the study’s data analysis results. The research zeroes in on the dissection and analysis of the integral elements that structure the provision of public digital cultural services, and it concentrates on the associated data computation. The conclusions drawn herein are expected to serve as a significant point of reference for ongoing academic investigations and practical explorations in affiliated domains.

Design/methodology/approach

In this research, the authors utilize a hybrid methodology to meticulously evaluate the efficiency of the components that underpin the provision of public digital cultural services (PDCS) in China. The authors embark on deconstructing the various constituents within the PDCS supply framework, conducting in-depth analyses and providing cogent interpretations of each integral element. Subsequently, the authors deploy the well-regarded SBM super-efficiency model to ascertain the operational efficiency of these components. Ultimately, through a comprehensive interpretation of the measured data and the integration of extant societal development conditions, the authors put forth relevant recommendations.

Findings

The provision of PDCS in China as of 2021 had been characterized by overall good efficiency, significant regional disparity and a disconnect between inputs and outputs with weak correlations to economic and demographic data.

Originality/value

In this study, the authors provide an exhaustive deconstruction and interpretation of the public digital cultural services supply system, thereby proposing a framework for evaluating the efficiency of supply element allocation. Additionally, the authors have determined a set of distinct measurable indicators that are readily accessible for open collection. Notably, this analytical and evaluative framework designed for element analysis and measurement may also find application in efficiency evaluation research of the supply systems of other related cultural endeavors.

Revolutionizing oncology care: pioneering AI models to foresee pneumonia-related mortality

BACKGROUND

Pneumonia is a leading cause of morbidity and mortality among patients with cancer, and survival time is a primary concern. Despite their importance, there is a dearth of accurate predictive models in clinical settings. This study aimed to determine the incidence of pneumonia as a cause of death in patients with cancer, analyze trends and risk factors associated with mortality, and develop corresponding predictive models.

METHODS

We included 26,938 cancer patients in the United States who died from pneumonia between 1973 and 2020, as identified through the Surveillance, Epidemiology, and End Results (SEER) program. Cox regression analysis was used to ascertain the prognostic factors for patients with cancer. The CatBoost model was constructed to predict survival rates via a cross-validation method. Additionally, our model was validated using a cohort of cancer patients from our institution and deployed via a free-access software interface.

RESULTS

The most common cancers resulting in pneumonia-related deaths were prostate (n=7300) and breast (n=5107) cancers, followed by lung and bronchus (n=2839) cancers. The top four cancer systems were digestive (n=5882), endocrine (n=5242), urologic (n=5198), and hematologic (n=3104) systems. The majority of patients were over 70 years old (57.7%), and 54.4% were male. Our CatBoost model demonstrated high precision and accuracy, outperforming other models in predicting the survival of cancer patients with pneumonia (6-month AUC=0.8384,1-year AUC=0.8255,2-year AUC=0.8039, and 3-year AUC=0.7939). The models also revealed robust performance in an external independent dataset (6-month AUC=0.689; 1-year AUC=0.838; 2-year AUC=0.834; and 3-year AUC=0.828). According to the SHAP explanation analysis, the top five factors affecting prognosis were surgery, stage, age, site, and sex; surgery was the most significant factor in both the short-term (6 months and 1 year) and long-term (2 years and 3 years) prognostic models; surgery improved patient prognosis for digestive and endocrine tumor sites with respect to both short- and long-term outcomes but decreased the prognosis of urological and hematologic tumors.

CONCLUSION

Pneumonia remains a major cause of illness and death in patients with cancer, particularly those with digestive system cancers. The early identification of risk factors and timely intervention may help mitigate the negative impact on patients' quality of life and prognosis, improve outcomes, and prevent early deaths caused by infections, which are often preventable.

Construction and interpretation of weight-balanced enhanced machine learning models for predicting liver metastasis risk in colorectal cancer patients

BACKGROUND

Colorectal cancer (CRC) is a major contributor to cancer-related mortality, with liver metastases developing in approximately 25% of affected individuals. The presence of liver metastasis significantly deteriorates the prognosis for patients. The objective of this study is to predict liver metastasis in CRC patients by developing machine learning (ML)-based models, thereby aiding clinicians in the decision-making process for appropriate interventions.

METHODS

Retrospective analysis was performed using the Surveillance, Epidemiology, and End Results (SEER) database, and cases with CRC from 2010 to 2015 were extracted to the downstream analysis. Logistic regression (LR), Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and LightGBM are applied to develop machine learning (ML) models to predict liver metastasis of CRC patient. To optimize the models, an improved weight-balancing algorithm was employed, enhancing the performance of the classifiers. The six models were tenfold cross-validated, and the optimal model was selected based on a combination of performance metrics. Shapley additive explanation (SHAP) was utilized to interpret the best-performing ML models globally, locally, and interactively. To ensure the model's reliability and generalizability, an external validation cohort of CRC cases from 2018 to 2021, obtained from a separate SEER database, was used for external evaluation.

RESULTS

In total, 50,062 patients with CRC were included in the analysis, with 5604 patients occurring liver metastasis. Among the six models evaluated, the CatBoost model showed excellent performance with the highest AUC of 0.8844. Moreover, the CatBoost model also outperformed the others in terms of recall (0.8060) and F1-score (0.6736). SHAP-based summary and force plots were used to interpret the CatBoost model. The interpretability analysis revealed that elevated carcinoembryonic antigen (CEA) levels, systemic therapy, N and T stages, and chemotherapy performed were the most significant indicators for predicting liver metastasis according to the optimal model. Furthermore, systemic therapy was suggested to increase liver metastasis risk in N0 stage patients, while it appeared to be beneficial in patients with lymph node metastasis. Preoperative radiation therapy was found to be more effective than postoperative radiation therapy. Validation using an external cohort of CRC cases from 2018 to 2021 further confirmed the robustness and stability of the CatBoost model, as its overall performance remained consistent with the internal validation results.

CONCLUSION

Elevated levels of carcinoembryonic antigen (CEA) have been identified as a crucial clinical predictor for liver metastasis in CRC patients. Furthermore, the administration of systemic therapy to patients who do not exhibit lymph node involvement has been found to increase the risk of liver metastasis. In terms of radiation therapy, preoperative radiation appears to be more efficacious in controlling the risk of liver metastasis compared to postoperative radiation. This finding underscores the importance of optimizing treatment strategies based on the specific clinical context and patient characteristics.