以格林模式为指导的健康宣教对尖锐湿疣患者情绪及自我效能的影响

目的探讨以格林模式为指导的健康宣教对尖锐湿疣患者情绪及自我效能的干预作用。方法将220例尖锐湿疣患者按就诊时间分为两组,各110例。对照组予以常规护理干预,观察组在此基础上实施以格林模式为指导的健康宣教,观察6个月,治疗后随访6个月统计复发率。干预前后采用医院自制尖锐湿疣患者健康知识调查问卷调查健康知识掌握情况,采用焦虑自评量表、抑郁自评量表评定焦虑、抑郁情绪,采用医学应对问卷评定应对方式,采用一般自我效能感量表评定自我效能。干预后采用纽卡斯尔护理满意度量表评定护理满意度。结果干预后观察组尖锐湿疣患者健康知识调查问卷总分及尖锐湿疣疾病知识、自我保健知识评分显著高于对照组(P<0.01),焦虑自评量表、抑郁自评量表评分显著低于对照组(P<0.01)。干预后观察组一般自我效能感量表评分及医学应对问卷的面对、自我效能评分显著高于对照组(P<0.01),回避、屈服评分显著低于对照组(P<0.01)。观察组复发率显著低于对照组(P<0.05),总满意率显著高于对照组(P<0.01)。结论以格林模式为指导的健康宣教能提高尖锐湿疣患者健康知识掌握度及自我效能水平,有效缓解其不良情绪,改变其应对方式,降低复发率,提高患者满意度。     

A multi-label approach using binary relevance and decision trees applied to functional genomics

Many classification problems, especially in the field of bioinformatics, are associated with more than one class, known as multi-label classification problems. In this study, we propose a new adaptation for the Binary Relevance algorithm taking into account possible relations among labels, focusing on the interpretability of the model, not only on its performance. Experiments were conducted to compare the performance of our approach against others commonly found in the literature and applied to functional genomic datasets. The experimental results show that our proposal has a performance comparable to that of other methods and that, at the same time, it provides an interpretable model from the multi-label problem.     

Filtering big data from social media – Building an early warning system for adverse drug reactions

ObjectivesAdverse drug reactions (ADRs) are believed to be a leading cause of death in the world. Pharmacovigilance systems are aimed at early detection of ADRs. With the popularity of social media, Web forums and discussion boards become important sources of data for consumers to share their drug use experience, as a result may provide useful information on drugs and their adverse reactions. In this study, we propose an automated ADR related posts filtering mechanism using text classification methods. In real-life settings, ADR related messages are highly distributed in social media, while non-ADR related messages are unspecific and topically diverse. It is expensive to manually label a large amount of ADR related messages (positive examples) and non-ADR related messages (negative examples) to train classification systems. To mitigate this challenge, we examine the use of a partially supervised learning classification method to automate the process.MethodsWe propose a novel pharmacovigilance system leveraging a Latent Dirichlet Allocation modeling module and a partially supervised classification approach. We select drugs with more than 500 threads of discussion, and collect all the original posts and comments of these drugs using an automatic Web spidering program as the text corpus. Various classifiers were trained by varying the number of positive examples and the number of topics. The trained classifiers were applied to 3000 posts published over 60 days. Top-ranked posts from each classifier were pooled and the resulting set of 300 posts was reviewed by a domain expert to evaluate the classifiers.ResultsCompare to the alternative approaches using supervised learning methods and three general purpose partially supervised learning methods, our approach performs significantly better in terms of precision, recall, and the F measure (the harmonic mean of precision and recall), based on a computational experiment using online discussion threads from Medhelp.ConclusionsOur design provides satisfactory performance in identifying ADR related posts for post-marketing drug surveillance. The overall design of our system also points out a potentially fruitful direction for building other early warning systems that need to filter big data from social media networks.     

Using Linked Data for polarity classification of patients’ experiences

Polarity classification is the main subtask of sentiment analysis and opinion mining, well-known problems in natural language processing that have attracted increasing attention in recent years. Existing approaches mainly rely on the subjective part of text in which sentiment is expressed explicitly through specific words, called sentiment words. These approaches, however, are still far from being good in the polarity classification of patients’ experiences since they are often expressed without any explicit expression of sentiment, but an undesirable or desirable effect of the experience implicitly indicates a positive or negative sentiment.This paper presents a method for polarity classification of patients’ experiences of drugs using domain knowledge. We first build a knowledge base of polar facts about drugs, called FactNet, using extracted patterns from Linked Data sources and relation extraction techniques. Then, we extract generalized semantic patterns of polar facts and organize them into a hierarchy in order to overcome the missing knowledge issue. Finally, we apply the extracted knowledge, i.e., polar fact instances and generalized patterns, for the polarity classification task. Different from previous approaches for personal experience classification, the proposed method explores the potential benefits of polar facts in domain knowledge aiming to improve the polarity classification performance, especially in the case of indirect implicit experiences, i.e., experiences which express the effect of one entity on other ones without any sentiment words.Using our approach, we have extracted 9703 triplets of polar facts at a precision of 92.26 percent. In addition, experiments on drug reviews demonstrate that our approach can achieve 79.78 percent precision in polarity classification task, and outperforms the state-of-the-art sentiment analysis and opinion mining methods.     

儿童风湿病国际相关诊治指南系列解读之七——儿童大动脉炎分类标准解读

大动脉炎（TA）是由免疫机制介导的慢性、肉芽肿性、炎症性疾病，病变主要累及主动脉及其一级分支。现今儿童TA诊断多采用由欧洲抗风湿病联盟、欧洲儿童风湿病学会和儿科风湿病国际研究组织共同制定的分类标准。影像学检查是诊断TA及评估疾病活动程度的重要方法。欧洲儿童风湿病单中心及访问点所发布的共识对该标准做了进一步补充。     

Prevalence of partial edentulism and RPD design in patients treated at College of Dentistry,Imam Abdulrahman Bin Faisal University,Saudi Arabia

This study aimed to investigate the prevalence of partial edentulism, RPD type, design, and components and their frequency of use by patients at the prosthodontic clinics of the College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia. The prepared surveys, laboratory authorization forms, and images of the RPD metal frameworks on casts were used for data collection. Two calibrated investigators studied the digital photographs to identify the Kennedy classification, type of RPD, major connector, clasp assembly, and other details. Data was collected and analyzed statistically. The results showed that the most common class of partial edentulism was Kennedy class I, whereas class IV was the least (p < 0.001). Sixty two percent of fabricated RPDs had metal frameworks, whereas 37.2% were frameless. RPI was the most frequently used clasp assembly (38.9%), a significant finding in Kennedy class I(p < 0.01). The maxillary anteroposterior palatal strap and mandibular lingual plate were the most commonly used major connectors, at 41.2% and 60.8%, respectively. Conclusions: Simple RPD design that accomplishes the treatment objectives as well as proper communication with a well-trained dental technician would promote the success of RPDs.     

Imaging of COVID-19: An update of current evidences

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been reported as a global emergency. As respiratory dysfunction is a major clinical presentation of COVID-19, chest computed tomography (CT) plays a central role in the diagnosis and management of patients with COVID-19. Recent advances in imaging approaches using artificial intelligence have been essential as a quantification and diagnostic tool to differentiate COVID-19 from other respiratory infectious diseases. Furthermore, cardiovascular involvement in patients with COVID-19 is not negligible and may result in rapid worsening of the disease and sudden death. Cardiac magnetic resonance imaging can accurately depict myocardial involvement in SARS-CoV-2 infection. This review summarizes the role of the radiology department in the management and the diagnosis of COVID-19, with a special emphasis on ultra-high-resolution CT findings, cardiovascular complications and the potential of artificial intelligence.     

基于协同过滤算法的中医智能问诊系统研究＊

目的研究中医智能问诊系统,实现快速获取关键症状并完成辨证,为中医问诊智能化、客观化提供了一种新的思路和方法。方法采用基于物品的协同过滤推荐算法(Item-Based Collaborative Filtering,ItemCF)和遗传算法构建症状获取模块以获取患者的症状,利用随机森林算法构建分类器并基于获取到的症状完成中医辨证。结果该系统实现了高效地获取患者症状并完成中医辨证。在13次提问次数下,便能获得辨证所需的核心症状,实现证候分类器90%以上的辨证效果。结论该问诊系统能够较好地解决中医问诊中"问什么、怎么问"的两个核心问题,相比依据问诊量表获取症状,极大地简化了问诊中关键症状获取的过程,并能够在证候分类中保持较好的分类效果,在问诊客观化研究上具有一定的实用价值。