Identifying the similarity and differences of malpractice,medical negligence and medical error has realistic meaning to deal with medical disputes efficiently.They are all subjective negligence and belong to the category of medical behavior.This paper mainly discussed the criteria and civil liability of their differences.

Pulse recognition is an important part of the objectification and intelligence of TCM.This non-invasive and fast diagnostic method has great clinical value,however,data imbalance and cumbersome feature extraction are still challenging problems.The feature vectors were extracted from the one-dimensional pulse signal obtained after the Butterworth bandpass filter using the tsfresh library.And 9 columns of medical auxiliary features selected by exploratory data analysis were added.The feature filtering is performed jointly to derive 21 columns of feature vectors,which are used as input to the weighted soft voting fusion model.The data imbalance problem is solved by Borderline SMOTE algorithm.Construct a weighted soft-voting fusion model based on four types of machine learning:XGBoost,RF,LGBM,and GBDT.Eventually,the models will output specific pulse categories and demonstrate the performance by evaluating the metrics accuracy,precision,recall and F1 score.The experimental results show that the screened 21 feature vectors for a total of six types of pulse signal test sets achieve an accuracy of 90.04%in the five-fold cross-validation and take only 65.9466 seconds.It can provide a more accurate and intelligent auxiliary reference for pulse signal recognition,with lower operational complexity and higher accuracy compared to commonly used pulse recognition methods.The shorter training time also makes it more clinically useful in the recognition of multiple pulse signals.

Objective To investigate the distribution differences of human papillomavirus(HPV) genotypes in the patients with cervical intraepithelial neoplasia grade Ⅱ/Ⅲ and the patients with cervical cancer. Methods 260 cases of cervical cancer and 280 cases of cervical intraepithelial neoplasia grade Ⅱ /Ⅲ patients who were admitted to our hospital from January 2010 to October 2017 were selected and divided into cancer group and neoplasia group respectively. The HPV infection of patients was detected and typing identification was performed. The distribution differences of HPV genotypes in cancer group and neoplasia group were compared. The differences of genotypes of HPV infection in different types of tissues in cancer group were also compared. Results The negative constituent ratio of HPV infection in cancer group was lower than that in neoplasia group(P＜0. 05), and the constituent ratio of all the high-risk types in single infection was significantly higher than that in the neoplasia group(P＜0. 05). The constituent ratio of all the low-risk types in single infection was significantly similar to that in the neoplasia group(P＞0. 05). The constituent ratio of multiple infections in cancer group was significantly higher than that in neoplasia group(P＜0. 05); the constituent ratios of negative HPV infections and 18 types of single infections in cervical squamous cell carcinoma was much lower than that of cervical adenocarcinoma(P＜0. 05). The constituent ratios of 16 types of HPV and multiple infections in cervical squamous cell carcinoma were much higher than that of cervical adenocarcinoma(P＜0. 05). Conclusion The risk of HPV infection in cervical cancer patients is significantly higher than that of cervical intraepithelial neoplasia grade Ⅱ /Ⅲ, the infection rate of cervical cancer high-risk type HPV is higher, and the risk of multiple infections is also higher. The tissue types of cervical cancer can be determined according to HPV typing.