[Exploration and example interpretation of real-world herbal prescription classification based on similarity matching algorithm].

In observational studies, herbal prescriptions are usually studied in the form of "similar prescriptions". At present, the classification of prescriptions is mainly based on clinical experience judgment, but there are some problems in manual judgment, such as lack of unified criteria, labor consumption, and difficulty in verification. In the construction of a database of integrated traditional Chinese and western medicine for the treatment of coronavirus disease 2019(COVID-19), our research group tried to classify real-world herbal prescriptions using a similarity matching algorithm. The main steps include 78 target prescriptions are determined in advance; four levels of importance labeling shall be carried out for the drugs of each target prescription; the combination, format conversion, and standardization of drug names of the prescriptions to be identified in the herbal medicine database; calculate the similarity between the prescriptions to be identified and each target prescription one by one; prescription discrimination is performed based on the preset criteria; remove the name of the prescriptions with "large prescriptions cover the small". Through the similarity matching algorithm, 87.49% of the real prescriptions in the herbal medicine database of this study can be identified, which preliminarily proves that this method can complete the classification of herbal prescriptions. However, this method does not consider the influence of herbal dosage on the results, and there is no recognized standard for the weight of drug importance and criteria, so there are some limitations, which need to be further explored and improved in future research.

A novel data-driven decision model based on extended belief rule base to predict China's carbon emissions.

Global warming and climate change are gaining traction in recent years. As a major cause of global warming, carbon emissions were centered to China's climate change policy initiatives. Nevertheless, the existing policy discourse has yet reached a consensus on the optimal modeling method for carbon emissions prediction that is well-informed of both policy goals and the time-series pattern of carbon emissions. This paper fills the gap by promoting a novel data-driven decision model for carbon emissions prediction that is based on the extended belief rule base (EBRB) inference model. The new decision model consists of three components: 1) an indicator integration method, which aims to generate a few group indicators from a large number of statistical indicators; 2) a new EBRB construction method, which aims to consider the management policy goals for constructing EBRB; 3) a new ER-based inference method, which aims to predict carbon emissions based on time series change of relevant factors. The effectiveness of the proposed decision model has been tested against carbon emissions management data from 30 provinces in China. Experimental results demonstrate that the model will offer powerful reference value in the policy decision-making process, which will help to meet policy requirements for carbon emissions.

Effect of Chinese Medicine in Patients with COVID-19: A Multi-center Retrospective Cohort Study.

OBJECTIVE: To evaluate the effectiveness and safety of Chinese medicine (CM) in the treatment of coronavirus disease 2019 (COVID-19) in China. METHODS: A multi-center retrospective cohort study was carried out, with cumulative CM treatment period of ⩾3 days during hospitalization as exposure. Data came from consecutive inpatients from December 19, 2019 to May 16, 2020 in 4 medical centers in Wuhan, China. After data extraction, verification and cleaning, confounding factors were adjusted by inverse probability of treatment weighting (IPTW), and the Cox proportional hazards regression model was used for statistical analysis. RESULTS: A total of 2,272 COVID-19 patients were included. There were 1,684 patients in the CM group and 588 patients in the control group. Compared with the control group, the hazard ratio (HR) for the deterioration rate in the CM group was 0.52 [95% confidence interval (CI): 0.41 to 0.64, P<0.001]. The results were consistent across patients of varying severity at admission, and the robustness of the results were confirmed by 3 sensitivity analyses. In addition, the HR for all-cause mortality in the CM group was 0.29 (95% CI: 0.19 to 0.44, P<0.001). Regarding of safety, the proportion of patients with abnormal liver function or renal function in the CM group was smaller. CONCLUSION: This real-world study indicates that the combination of a full-course CM therapy on the basic conventional treatment, may safely reduce the deterioration rate and all-cause mortality of COVID-19 patients. This result can provide the new evidence to support the current treatment of COVID-19. Additional prospective clinical trial is needed to evaluate the efficacy and safety of specific CM interventions. (Registration No. ChiCTR2200062917).