Objective Empathy between doctors and patients is crucial in enhancing patient satisfaction with medical consultations. This study, grounded in empathy theory, employs natural language processing and machine learning algorithms to explore the factors influencing patient satisfaction in online healthcare services, particularly the impact of doctor-patient empathy.Methods Utilizing the three dimensions of the Jefferson Scale of Physician Empathy, seven variables were extracted from patient-doctor dialogs as independent variables, with patient satisfaction as the dependent variable. Employing machine learning algorithms, a classification model was constructed to identify the best-fitting model for exploring the pivotal factors influencing patient satisfaction in online medical services. The optimal model was then chosen to investigate the essential factors impacting patients' satisfaction with online healthcare.Results A total of 7586 data points were collected, with 5447 consultation dialogs (71.8%) receiving a satisfactory rating from patients. LightGBM emerged as the best-performing model, achieving an F1 score of 0.78 and an area under the curve value of 0.81. Factors within the Standing in Patient's Shoes and Perspective Taking dimensions were identified as key determinants of patient satisfaction in online healthcare services.Conclusion This study broadens the conventional scope of applying empathy theory, signifying its crucial role in cultivating doctor-patient empathy within the realm of online healthcare and elevating the overall quality of medical services. The findings indicate that two pivotal factors influencing patients' satisfaction with online healthcare are doctors' perceived competence and ability to empathize, understanding patients' perspectives, and offering assistance.

To investigate how chronic obstructive pulmonary disease is treated using traditional Chinese medicine's drug guidelines. By organizing medical records from the past and present,a database was established, and SPSS and Python were used to perform drug frequency, nature, taste, meridians and efficacy statistics. Data mining was performed in combination with association rules, clustering and complex network analysis. A total of 837 medical records from 161 ancient books and documents were included, involving 629 prescriptions and 667 Chinese medicines, with a total frequency of use of 7314 times. High-frequency drugs include Liquorice Root, Semen Armeniacae Amarum, Cortex Cinnamomi, ginseng, Rhizoma Rhizoma Pinelliaee, Pericarpium Citri Reticulatae, Ephedra, and Cortex Mori. The medications' meridians are primarily lung meridians, their characteristics are primarily warm and neutral, and their flavor is primarily pungent and bitter. Intestinal moisture and constipation relief, heat removal and detoxification, drying dampness and phlegm resolution, expectorant, and coughing are the main effects. Association rule analysis found that core drug pairs such as Ephedra -> Liquorice Root, Ephedra -> Semen Armeniacae Amarum, etc. Cluster analysis divided the top 30 high-frequency drugs into four categories, and complex network analysis screened out the core formula, which included Liquorice Root, Cortex Cinnamomi, Semen Armeniacae Amarum, ginseng, Rhizoma Rhizoma Pinelliaee, Ephedra, and Pericarpium Citri Reticulatae. The goal of TCM treatment for COPD is to address the underlying cause as well as its symptoms. The core formula is centered on Liquorice Root, and treatment can be based on the patient's symptoms.

基于"好大夫在线"网站先天性心脏病医疗服务数据和患者评价数据,采用数据分析和可视化方法,阐述在线医疗网站先天性心脏病医疗服务现状,并针对平台机制完善提出建议.

Objective To investigate the service evaluation status of TCM doctors on internet inquiry platform, and to provide reference for the improvement of service quality and medical management.Methods Association rule mining and content analysis were used to analyze the service data of TCM doctors on internet inquiry platform. Results The mean score of online TCM doctor service was 9.76, and user satisfaction was generally high. Among them, the resident physicians got the highest score（9.85）, which was significantly different from chief physicians（9.56）. The scores of endocrinology and acupuncturist were 9.87and 9.83, respectively, which were significantly higher than those of obstetrics and gynecology and andrology,9.53 and 9.56, respectively. Doctors who specialize in chronic diseases such as diabetes, hypertension and coronary heart disease were more likely to receive high scores. Patients’ evaluation of doctors mainly focused on five dimensions: doctors’ attitude, doctors ’ medical skills, inquiry time, inquiry process and diagnosis and treatment conditions. Conclusion The service quality and user satisfaction of online TCM doctors are generally good, but there are still problems such as poor consultation results and slow response time. Improving the service quality of doctors on online medical website can be guaranteed by improving the inquiry ability of online TCM doctors, strengthening the construction of online TCM specialty and dominant diseases, and introducing diversified service quality evaluation indexes of TCM doctors.

基于乔哈里视窗理论,从开放区、隐秘区和盲目区三个维度对海量医患交互数据进行聚类与界定,得到 5 种医患交互模式:沟通不足型、封闭交流型、患者主导型、患者独白型和医患平等型.采用统计分析和会话分析方法进一步研究发现:医生回复时间、患者扩展回答质量、医生回答详尽及明确程度、医患问答的话轮交叠度是不同模式下影响患者满意度的关键因素.最后,从信息质量、会话结构、生命关怀、信任关系四个层面针对三方主体提出建议,以期为提升医患交互质量、构建和谐医患关系、提供优质在线问诊服务提供思路.

[目的/意义]本研究旨在从研究主题、方法等多个角度分析智慧养老的研究现状和热点趋势,为智慧养老未来发展提供方向.[方法/过程]首先,基于词频统计扩展智慧养老相关关键词,于CNKI数据库中获取与智慧养老密切相关的574篇文献;其次采用文献计量、社会网络分析、LDA主题聚类与内容分析方法,对发文趋势、核心发文主体、研究主题、研究方法进行探析,并运用Python的Echarts工具实现智慧养老领域的多维度可视化分析.[结果/结论]智慧养老研究热度总体呈现攀升趋势,且形成了以高校内部合作为主的核心发文团体;研究主题主要包括智慧养老服务模式、技术需求与接受度、信息技术应用效果和智能养老产品设计,其中技术类、政策类关键词不断拓展丰富,紧随时代热点;研究方法的使用呈现多元化态势,但仍以定性分析为主,实证研究较少,科学性上有所欠缺.

[目的]对在线健康社区用户进行精准画像并准确预测其在社区中的参与情况,有助于社区管理者早期识别流失用户,并做出个性化挽留措施.[方法]构建多维度用户画像标签体系,采用统计分析、社会网络分析、自然语言处理技术、LDA主题聚类实现指标计算与可视化;将用户画像标签数据作为用户流失预测的模型输入,构建了基于滑动窗口的用户流失实时预测模型.[结果]以华夏中医论坛的真实数据进行实证研究,为9 918个用户生成了多维度画像标签,构建并比较多种机器学习算法对用户流失的预测效果,结果显示Gradient Boosting算法效果最佳,F1值达到88.77％.[局限]未在更多在线健康社区中应用,用户数据量较少.[结论]本研究提出了一种依据用户在线交互行为特征实现多维度用户画像标签计算的方法,并验证了用户画像在用户流失预测中的应用可行性.

随着信息技术和医疗服务水平的提升,越来越多的患者开始通过在线医疗网站进行挂号就诊.基于这一因素的影响,患者对于在线医疗网站的需求量与日俱增,同时产生了大量的就医数据.本文通过采集在线医疗网站的数据信息,运用文本分析、用户画像模型及机器学习等技术手段,分析影响患者就医体验的因素以及不同因素的影响程度.根据研究结果 完善在线医疗网站的医师推荐机制及医师服务准则,促使医生提供更加优质的服务,提高患者对在线医疗网站的使用体验.

介绍大数据相关专业课程体系研究现状,基于扎根理论,针对大学生需求构建中医药大数据管理与应用专业课程体系理论模型,详细阐述模型构建方法、结构,为相关研究提供参考.

运用文献分析法、词频分析法得出健康传播近十年的研究热点为新媒体、健康教育、健康素养、医患关系.新媒体对于健康传播有一定的促进作用,如扩大了传播的可及范围、提高了公众的接受程度等.同时,新媒体环境中的健康传播也存在信息质量难以保障、市场失序等问题.我国应通过提高新媒体环境下社会伦理失范与违法成本;加强政府监管,建立商业伦理规范;完善质量评估体系,进行风险评估前置;发挥新媒体优势,促进健康知识的精准传播与广泛传播等措施优化健康传播体系,以发挥其在健康促进、健康教育等方面的巨大作用.

Objective To study the rule of Chinese medicine prescription According to the prescription novel coronavirus pneumonia; to provide reference for the treatment of epidemic diseases. Methods Through crawling 227 prescriptions of Xinguan TCM collected by Huabing data website intelligent TCM big data platform, we analyzed the web page data by using word cloud analysis, data visualization and the third-party library lxml and request of Python. Results High frequency of drug use of traditional Chinese medicine are: Huoxiang, Atractylodes, Platycodon, honeysuckle, astragalus, Scutellaria, Atractylodes macrocephala, etc. The analysis of clinical symptoms showed that the most common symptoms were fatigue, fever, white fur, cough, chest tightness, diarrhea and so on. Hebei, Sichuan, Heilongjiang, Gansu and other provinces provide more. Conclusion The novel coronavirus pneumonia and almond novel coronavirus pneumonia treatment are better. The results showed that the effective prescriptions and fever, fatigue and other common clinical manifestations, as well as the provinces with higher prescriptions, have important reference significance for the follow-up development of the new crown pneumonia.

以在线医疗网站患者对医生的评价数据为基础,基于jieba分词技术从医生基本信息、医生所在医院、患者评价信息3个维度构建用户画像,借助PowerBI实现用户画像可视化,分析患者对医生的关注侧重点,为智能医生推荐奠定基础.

针对中医药大学公共选修程序设计课程所面临的问题,通过问卷调查分析课程目标定位、选课学生特点,提出以混合教学理论为指导,选择Python语言作为公共选修程序设计课程的零基础入门语言,采用“MOOC+SPOC+翻转课堂”的混合教学模式的教学思路,并进行了教学实践探索和总结.

通过回顾CBL教学法和慕课现状,运用调查法、文献研究法进行资料收集,并通过描述性研究,得出设计基于CBL教学法的医学慕课辅助网站的必要性及方法.所设计慕课辅助网站拟基于循证医学的思想,"以症状体征为分类,以临床思维为要点,以NLP技术为核心",以真实案例及虚拟患者构建良好的交互体验学习过程,加入互动学习、同行评议与系统检验评估等方法,建设一个较为开放的帮助医疗学习者锻炼临床医学思维的平台.

分析医学院校管理信息系统课程教学中存在的问题,提出教学改革具体措施,包括根据专业特点重新规划教学体系、动态更新教学内容、重视“互联网+”为导向的实践环节、采用多样化教学方式等,有助于提高课程教学质量和效果,提升学生实际应用能力.

1 计算机基础课程实验教学存在的问题 计算机基础是面向我校大一学生开设的一门通识必修课程,旨在培养学生的医学信息素养和实践操作能力,目前采用的是基于BB平台的翻转课堂教学模式.教学内容不再限于某一种软件的学习,而是分为9个不同的知识模块.

Background: Whether it is a single genetic disease, multiple genetic diseases or acquired genetic diseases, the discovery and prediction of disease-causing genes is a difficult problem in biomedical industry. It has also become an important research task in biomedical text mining. Methods: In the early stage of the prediction of disease-causing genes, the researchers predicted two major methods of linkage analysis and association analysis. Conclusion: The prediction of disease-causing genes can be predicted by using different prediction methods and different bioinformatics data according to the different problems of research questions and the different situations of concern. At present, the researchers have developed a number of related prediction tools to help understand, detect and predict disease-causing genes and pathogenesis of disease and other issues. Prediction of disease-causing genes is still a difficult and meaningful work.

Based on web of science retrieval tools, bibliometric analysis published on the world through the research field of 2007-2017 years of the coronary heart disease, combined with CiteSpace and Excel analyze the development of coronary heart disease in the research field of Excel software, the research status (including research methods, basic theoretical research and applied research, etc.) and the future development trend, key areas summarize the research of coronary heart disease, and puts forward some suggestions on the future research focus.

The paper analyzes the background of teaching reform of management information system course in medical colleges and universities by introducing reform situation in the aspects like course setting and contents and elaborating on teaching organization form,including optimization of teaching cases,combination of experiment with practice,introduction of self learning mode,optimization of teaching evaluation and other aspects.

MOOC has attracted many learners from all over the world with the advantages of open resources, low registration threshold and independent learning. As a new online education model, MOOC is a challenge and opportunity for the traditional higher education. SPOC is the product of the development of MOOC. It combines MOOC with traditional campus teaching effectively, and it is a blended teaching model based on MOOC. It has the characteristics of Small-scale Specific groups and online open. SPOC promote the brand effect of the University and improvement the teaching quality of school cost. This paper briefly introduces the development of MOOC and SPOC, expounds the concepts and characteristics of MOOC and SPOC, and discusses the implementation and application of MOOC+SPOC based blended teaching model in College teaching.