Background Within-day glycemic variability (GV), characterized by frequent and significant fluctuations in blood glucose levels, is a growing concern in hospitalized patients with type 2 diabetes mellitus (T2DM). It is associated with an increased risk of hypoglycemia and potentially higher long-term mortality rates. Robust clinical evidence is needed to determine whether traditional Chinese medicine (TCM) decoctions can be a beneficial addition to the management of within-day GV in this patient population. Methods This retrospective cohort study utilized data from adult inpatients diagnosed with T2DM admitted to the Traditional Chinese Medicine Hospital of Kaifeng. The primary outcome investigated was the association between the use of TCM decoctions and improved stability of within-day GV. Blood glucose variability was assessed using the standard deviation of blood glucose values (SDBG). For each patient, the total number of hospitalization days with SDBG below 2 mmol/L was calculated to represent within-day GV stability. Hospitalization duration served as the secondary outcome, compared between patients receiving TCM decoctions and those who did not. The primary analysis employed a multivariable logistic regression model, with propensity score matching to account for potential confounding variables. Results A total of 1,360 patients were included in the final analysis. The use of TCM decoctions was significantly associated with enhanced stability of within-day GV (OR = 1.77, 95% CI: 1.34-2.33, P < 0.01). This association was most prominent in patients with a diagnosis of deficiency syndrome (predominantly qi-yin deficiency, accounting for 74.8% of cases) and a disease duration of less than 5 years (OR = 2.28, 95% CI: 1.21-4.29, P = 0.03). However, TCM decoctions did not exert a statistically significant effect on hospitalization duration among patients with T2DM (OR = 0.96, 95% CI: 0.91-1.01, P = 0.22). Conclusion This study suggests that TCM decoctions may be effective in improving within-day GV stability in hospitalized patients with T2DM. This effect appears to be most pronounced in patients diagnosed with deficiency syndrome, particularly those with qi-yin deficiency and a shorter disease course. Further investigation is warranted to confirm these findings and elucidate the underlying mechanisms.

寻常型银屑病是一种临床常见的皮肤病,由于其具有损容性和无法根治等特点,发病往往给患者带来严重的精神和经济负担。中医药治疗寻常型银屑病历史悠久,且通过多年的临床观察发现中医药治疗寻常型银屑病疗效显著且安全性好。寻常型银屑病的中医药指南和专家共识由众多专家基于大量的证据制定而成,是目前诊疗思路和诊疗方法的总结和体现,可给予临床医生指导性的建议。而本体作为一种知识表达和组织模型,可实现知识的表达、融合和复用,并为具体的应用奠定知识基础。所以,构建以指南和专家共识为知识来源的寻常型银屑病中医诊疗本体知识库,可实现寻常型银屑病的知识表达、知识共享和复用,同时也可促进临床指南和专家共识的应用和推广,也有助于实现寻常型银屑病的中医辅助诊疗。研究目的根据中医药知识表达特点筛选出符合中医药表达特色的顶层本体,并以寻常型银屑病相关指南和专家共识为知识来源构建中医诊疗本体知识库,实现知识表达和推理,为中医药顶层本体和领域本体的构建以及本体复用和共享奠定基础。并在此基础上构建中医辅助诊疗系统,实现寻常型银屑病的临床辅助诊疗,同时也证明此本体知识库具有实用价值。并以此为例,探索中医药本体的构建和应用。研究方法根据中医表达特色和中医诊疗流程等,调研分析目前比较成熟的顶层本体,筛选出通用形式化本体（General Formal Ontology,GFO）作为中医知识表达模型;同时通过文献调研对比目前比较成熟的本体构建方法,确定以七步法为参考,指导寻常型银屑病本体知识库构建:首先确定构建目的为构建中医诊疗本体知识库,以实现寻常型银屑病的知识表达和辅助诊疗系统的构建,并以此为出发点,抽取指南和专家共识中的中医诊疗知识,构建本体类和类的层级,并根据指南知识和应用目的建立类的对象属性和数据属性,实现知识的语义表达和属性构建,最后添加具体实例,并使用protege自带的推理机HermiT 1.4.3.456检验本体的一致性,完成知识的表达和知识库的构建,并在此基础上利用D3工具插件,初步构建寻常型银屑病的辅助诊疗系统,实现寻常型银屑病中医诊疗本体知识库的应用。研究结果通过文献调研筛选出符合中医表达特点的GFO本体模型,并对指南和专家共识进行梳理,人工抽取中医诊疗相关知识,进行知识的整合,并在七步法的指导下构建了寻常型银屑病本体知识库（其中包括77个类、183个实例、20个对象属性、6个数据属性以及42个推理规则）,实现了指南和专家共识多个来源的知识整合和知识表达,同时,构建诊疗规则和合理用药提示规则,在此基础上构建辅助诊疗系统,初步实现了知识推理以及寻常型银屑病知识的可视化和知识检索、证型诊断、诊疗方案推荐和合理用药提示等。结论本体是一种有效的知识组织和知识表达模型,本文基于指南和专家共识构建寻常型银屑病中医本体知识库,实现了知识的整合、表达和推理,并在知识库的基础上构建了辅助诊疗系统,初步实现了本体知识库的应用,为中医药顶层本体和领域本体的构建以及中医药本体的应用做出探索性研究。但此本体知识库中术语的标准化、概念化程度有待提高,知识的来源需进一步丰富,系统的性能也需进一步改善,以使此本体知识库更加的规范,便于知识的共享和复用,以及辅助诊疗系统的推广和应用。

This paper addresses the mixture symptom mention problem which appears in the structuring of Traditional Chinese Medicine (TCM). We accomplished this by disassembling mixture symptom mentions with entity relation extraction. Over 2,200 clinical notes were annotated to construct the training set. Then, an end-to-end joint learning model was established to extract the entity relations. A joint model leveraging a multihead mechanism was proposed to deal with the problem of relation overlapping. A pretrained transformer encoder was adopted to capture context information. Compared with the entity extraction pipeline, the constructed joint learning model was superior in recall, precision, and F1 measures, at 0.822, 0.825, and 0.818, respectively, 14% higher than the baseline model. The joint learning model could automatically extract features without any extra natural language processing tools. This is efficient in the disassembling of mixture symptom mentions. Furthermore, this superior performance at identifying overlapping relations could benefit the reassembling of separated symptom entities downstream.

在某政府部门信息系统迁云过程中,论文总结出了2阶段15步迁云法,并从分层、纵深防御思想出发解决上云后的安全问题。然后介绍了迁云流程及取得的成效,分享了迁云的典型案例及迁云经验,为同行提供迁云参考经验。

本文研究利用XGBoost方法对潜在高血脂症进行预测,通过对体检数据进行训练,测试数据集的准确率、召回率以及F1值都较为理想。并通过对比XGBoost、随机森林以及逻辑回归三种方法的预测结果,结果表明XGBoost算法能够更准确的对潜在高血脂症进行预测,可以为潜在高血脂提供强有力的数据支撑。

Viral hepatitis B has become a worldwide public problem since it was discovered in the sera of Australian aboriginals in the 1960s. In addition, viral hepatitis B has the characteristics of a long course, difficult to cure, and recurring attacks. At present, western medicine adopts symptomatic treatment strategies including antiviral, liver protection and enzyme reduction, yellowing and improvement of body immunity, etc. However, long-term use of antibiotics is prone to drug resistance and adverse reactions to the kidneys. Compared with Western medicine's symptomatic treatment strategies, Chinese medicine prescriptions, such as Yinchenhao Decoction, Sini San, Ganlu Xiaodu Dan, etc., have the characteristics of multi-flavored Chinese medicine and multi-component synergistic effect, simultaneously targeting multiple targets and multiple pathways. It is easier to get wide affirmation from clinicians and patients. As a manifestation of physicians' clinical experience, clinical electronic medical records include a large amount of effective diagnosis and treatment knowledge, which is a valuable source of knowledge for clinical decision-making assistance. The entity uniqueness and relationship display of the knowledge map are consistent with the characteristics of traditional Chinese medicine knowledge that need to be named uniformly and emphasize the relationship. Using the knowledge map as the organizational form of traditional Chinese medicine knowledge will be able to well present the overall network of knowledge, and in the knowledge The function of clinical decision-making assistance is realized under the support of reasoning. This article takes viral hepatitis B as an example, carries out effective knowledge extraction from hepatitis B electronic medical record data, merges to form structured knowledge, and constructs a knowledge map of traditional Chinese medicine diagnosis and treatment of hepatitis B. Driven by the rules of knowledge reasoning, it provides knowledge support for intelligent diagnosis and treatment, auxiliary analysis, and decision support; summarizes and displays the experience of Chinese medicine diagnosis and treatment, so that inexperienced doctors can better learn and inherit. © 2021 IEEE.

In recent years, recommendation systems based on collaborative filtering (CF) have achieved a high performance. Most of the existing recommendation systems use similarity measures to determine the suitability of items for users based on latent factor models (LFM). However, these recommendation systems reduce the explainability of recommendations and hide the reasons for recommending specific items. As a result, users tend to distrust the recommendation results. To address this problem, we propose the neural explicit factor model (NEFM). Based on the user-item rating matrix, we propose adding both user-feature attention matrix and an item-feature quality matrix to improve the explainability of user and item vectors. In addition, a feedforward neural network and a one-dimensional convolutional neural network extract features from user, item and the item-feature vector. Finally, a prediction layer performs the inner product of user data, item data, and item features. Experiments on the MovieLens and Yahoo Movies datasets validate the proposed model, and comparisons with similar recommendation models show the higher accuracy and explainability of our proposal.

目的探索一种具有较好扩展性且可检验数据准确性的中医皮肤病"病-证-治"本体的存储方法,为未来开展大规模、高准确性领域本体存储的研究奠定基础。方法在明确中医皮肤病"疾病-证候-治疗"等领域本体及概念间关系的基础上,以资源描述框架（RDF）模型实现领域本体的规范化表达,应用由RDF模型向Neo4j属性图模型映射的规则对领域本体进行映射与存储,结合领域本体对属性的约束设置,最终实现对已存储数据的一致性检验以提高其准确性。结果构建了中医皮肤病"病-证-治"本体及其间关系的RDF模型,实现了由RDF模型向Neo4j属性图模型的映射,并对已存储数据实现了对象属性定义域、值域的数据一致性检验。结论基于Neo4j图数据库构建的中医皮肤病"病-证-治"本体具有扩展性较强、数据准确性较高的特点,本研究为后续进行大规模、高准确性存储中医药领域本体的研究奠定了基础。

随着互联网的迅猛发展,网上健康信息以几何速度增长,其中大量虚假健康信息给人们的生活带来了很大影响,但目前对虚假健康信息文本识别的研究非常缺乏,以往研究主要集中在识别微博上的谣言、伪造商品评论、垃圾邮件及虚假新闻等方面。鉴于此,采用基于词向量的深度神经网络模型和基于双向编码的语言表征模型,对互联网上流传广泛的健康信息文本进行自动分类,识别其中的虚假健康信息。实验中,深度网络模型比传统机器学习模型性能提高10%,融合Word2vec的深度神经网络模型比单独的CNN或Att-BiLSTM模型在分类性能上提高近7%。BERT模型表现最好,准确率高达88.1%。实验结果表明,深度学习可以有效识别虚假健康信息,并且通过大规模语料预训练获得的语言表征模型比基于词向量的深度神经网络模型性能更好。