Using AdaBoost Meta-Learning Algorithm for Medical News Multi-Document Summarization

Automatic text summarization involves reducing a text document or a larger corpus of multiple documents to a short set of sentences or paragraphs that convey the main meaning of the text. In this paper, we discuss about multi-document summarization that differs from the single one in which the issues of compression, speed, redundancy and passage selection are critical in the formation of useful summaries. Since the number and variety of online medical news make them difficult for experts in the medical field to read all of the medical news, an automatic multi-document summarization can be useful for easy study of information on the web. Hence we propose a new approach based on machine learning meta-learner algorithm called AdaBoost that is used for summarization. We treat a document as a set of sentences, and the learning algorithm must learn to classify as positive or negative examples of sentences based on the score of the sentences. For this learning task, we apply AdaBoost meta-learning algorithm where a C4.5 decision tree has been chosen as the base learner. In our experiment, we use 450 pieces of news that are downloaded from different medical websites. Then we compare our results with some existing approaches.

Unsupervised Graph-Based Tibetan Multi-Document Summarization

Text summarization creates subset that represents the most important or relevant information in the original content,which effectively reduce information redundancy.Recently neural network method has achieved good results in the task of text summarization both in Chinese and English,but the research of text summarization in low-resource languages is still in the exploratory stage,especially in Tibetan.What’s more,there is no large-scale annotated corpus for text summarization.The lack of dataset severely limits the development of low-resource text summarization.In this case,unsupervised learning approaches are more appealing in low-resource languages as they do not require labeled data.In this paper,we propose an unsupervised graph-based Tibetan multi-document summarization method,which divides a large number of Tibetan news documents into topics and extracts the summarization of each topic.Summarization obtained by using traditional graph-based methods have high redundancy and the division of documents topics are not detailed enough.In terms of topic division,we adopt two level clustering methods converting original document into document-level and sentence-level graph,next we take both linguistic and deep representation into account and integrate external corpus into graph to obtain the sentence semantic clustering.Improve the shortcomings of the traditional K-Means clustering method and perform more detailed clustering of documents.Then model sentence clusters into graphs,finally remeasure sentence nodes based on the topic semantic information and the impact of topic features on sentences,higher topic relevance summary is extracted.In order to promote the development of Tibetan text summarization,and to meet the needs of relevant researchers for high-quality Tibetan text summarization datasets,this paper manually constructs a Tibetan summarization dataset and carries out relevant experiments.The experiment results show that our method can effectively improve the quality of summarization and our method is competitive to previous unsupervised methods.

详解RStudio中使用lm函数及summary函数建模与模型检验的输出结果

使用RStudio,通过各种随机函数生成样本数据,再使用stats包的lm函数及summary函数建立线性回归模型,并对其输出结果的各项细则详细解读,叙述所用的理论与公式,并尝试用各种方法重新编程,从而对这个函数的建模原理得到更好的把握,能有助于更好地使用此函数建立合适的模型,并灵活地利用RStudio编程实现各种建模需要的输出结果。

Multi-Document Summarization Model Based on Integer Linear Programming

This paper proposes an extractive generic text summarization model that generates summaries by selecting sentences according to their scores. Sentence scores are calculated using their extensive coverage of the main content of the text, and summaries are created by extracting the highest scored sentences from the original document. The model formalized as a multiobjective integer programming problem. An advantage of this model is that it can cover the main content of source (s) and provide less redundancy in the generated sum- maries. To extract sentences which form a summary with an extensive coverage of the main content of the text and less redundancy, have been used the similarity of sentences to the original document and the similarity between sentences. Performance evaluation is conducted by comparing summarization outputs with manual summaries of DUC2004 dataset. Experiments showed that the proposed approach outperforms the related methods.

Discharge Summaries Based Sentiment Detection Using Multi-Head Attention and CNN-BiGRU

Automatic extraction of the patient’s health information from the unstructured data concerning the discharge summary remains challenging.Discharge summary related documents contain various aspects of the patient health condition to examine the quality of treatment and thereby help improve decision-making in the medical field.Using a sentiment dictionary and feature engineering,the researchers primarily mine semantic text features.However,choosing and designing features requires a lot of manpower.The proposed approach is an unsupervised deep learning model that learns a set of clusters embedded in the latent space.A composite model including Active Learning(AL),Convolutional Neural Network(CNN),BiGRU,and Multi-Attention,called ACBMA in this research,is designed to measure the quality of treatment based on discharge summaries text sentiment detection.CNN is utilized for extracting the set of local features of text vectors.Then BiGRU network was utilized to extract the text’s global features to solve the issues that a single CNN cannot obtain global semantic information and the traditional Recurrent Neural Network(RNN)gradient disappearance.Experiments prove that the ACBMA method can demonstrate the effectiveness of the suggested method,achieve comparable results to state-of-arts methods in sentiment detection,and outperform them with accurate benchmarks.Finally,several algorithm studies ultimately determined that the ACBMA method is more precise for discharge summaries sentiment analysis.

Summary of the Best Evidence for Preoperative Pre-Rehabilitation in Patients with Lung Cancer Complicated with COPD

Objective: To search, evaluate and integrate the best domestic and foreign evidence on the preoperative pre-rehabilitation of lung cancer patients with COPD, and summarize the best evidence. Methods: Cochrane Library, BMJ Best Practice, JBI Center for International Cooperation in Evidence-Based Health Care Library, and Up to were searched by computer system Date, Embase, CINAHL, PubMed, Evidence-Based Health Care Center Database (JBI), American Guidenet, Global Guidenet, CNKI, CMBM, Wanfang, and VIP databases on preoperative pre-rehabilitation of patients with lung cancer and COPD. Literature screening and quality evaluation were conducted independently by two researchers, and evidence was extracted, summarized and summarized according to the theme. Results: A total of 13 kinds of literature were included, including 4 guidelines, 4 systematic reviews, 3 evidence summaries, and 2 expert consensus articles. This paper summarizes 44 best evidence on pre-rehabilitation of lung cancer patients with COPD, including 8 aspects: pre-rehabilitation content and principle, pre-rehabilitation assessment, exercise rehabilitation, nutrition management, psychological guidance, health education, multidisciplinary cooperation, quality control and follow-up. Conclusion: The best evidence of preoperative pre-rehabilitation for patients with lung cancer complicated with COPD is rich in content, and needs to be selected according to the characteristics of the hospital and the clinical environment. Some of the evidence still needs to be confirmed by higher-quality studies. .

Source Text Borrowing in Summary of Integrated Writing

Integrated writing tasks,requiring writers to summarize source content and integrate their ideas or experience with those in the source text,are widely used in standardized tests of English proficiency.Integrated writing adds an element not found in traditional independent writing:the use of source text material.Recently,concern has been raised that over-reliance on source texts in integrated writing may lead to plagiarism as well as inaccurate assessment which calls for further investigation.Research on L2 students’writing using sources has broadened its focus from transgressive citing practices to stages of skill development in this complicated literacy,the successive challenges students face,and how instruction might be of benefit.Previous literature mainly focused on source use in academic writing and seldom considered summary writing in standardized tests,while summary strategy is particularly important in standardized tests,in which summary accounts for a significant part of the assessment criteria.Given that a series of previous literature have indicated that source text use,source texts,and prompts are associated with the quality of summary writing,this article reviews the source text borrowing in the summary of integrated writing to further explore the relationship between some variables and the quality of summary writing and therefore finds some research gaps in this field.

Summary of the Best Evidence for the Evaluation and Management of Chemotherapy-Related Nausea and Vomiting in Cancer Patients

Objective: To summarize the best evidence for the evaluation and management of chemotherapy-related nausea and vomiting in cancer patients, so as to promote the standardized management of chemotherapy-related nausea and vomiting in cancer patients. Methods: A computer search was conducted for all evidence on chemotherapy-associated nausea and vomiting interventions, including guidelines, expert consensus, best clinical practice information booklet, recommended practice, evidence summary, and systematic review. The search period is from April 30, 2022. After the literature quality evaluation, the evidence extraction and summary of the literature meeting the quality standards. Results: A total of 16 literatures were included, including 9 guidelines, 5 systematic reviews and 2 expert consensus papers. 46 pieces of best evidence on the assessment and management of chemotherapy related nausea and vomiting in cancer patients were summarized, including 6 aspects including risk assessment and management, non-drug management, drug management, multidisciplinary cooperation, education and training, and health education. Conclusion: This study summarized the current best evidence on the assessment and management of chemotherapy-related nausea and vomiting in cancer patients. Clinical staff should apply evidence according to specific clinical scenarios, professional skills and patients’ wishes in order to reduce the degree and incidence of chemotherapy-related nausea and vomiting in cancer patients and improve the quality of care.

AI赋能视角下的信息行为研究--2023年信息行为研究年会综述

2023年12月23—24日,2023年信息行为研究年会在北京大学召开,来自全国70多所高校的200余位知名专家学者和学生参加了会议。本次年会以“AI赋能视角下的信息行为研究”为主题,设置了主旨报告、专题讨论、海报展示、分论坛报告、眼动追踪工作坊等环节,围绕信息行为研究领域在新一代人工智能(AI)技术浪潮下的理论创新、方法应用、情境拓展、人本关怀、社会发展等方面进行深入研讨。文章综述了与会者的学术成果与主要观点,为信息行为研究的发展与创新提供理论启迪与研究指引。

外科术后患者口渴管理的最佳证据总结

目的总结外科术后患者口渴管理的相关证据,为临床护理人员处理口渴症状提供参考依据。方法以“6S”证据资源金字塔模型为基础,以“口渴/口干/口腔干燥/干渴”“术后/围术期/围手术期”“thirst/drymouth/xerostomia/thirsty/polydipsia/mouthdryness/hyposalivation/asialia”“perioperativeperiod/perioperative/postoperative/after operation/post operation”为中英文检索词,检索与外科术后口渴相关的实践指南、专家共识、证据总结、临床决策、推荐实践和系统评价,检索时限为建库至2022年12月31日,由2名研究人员独立开展文献筛选、质量评价和证据综合。结果共纳入11篇文献,包括1篇实践指南、3篇证据总结、4篇系统评价和3篇专家共识,分别从口渴评估、干预策略、效果评价、教育培训4个方面归纳出13条证据,其中证据等级1级7个、5级6个。结论临床护理人员应重视外科术后患者的口渴管理,及时准确评估患者的口渴症状、制订个性化干预策略以缓解其口渴,最大程度提升患者舒适度。

癫痫青少年至成人过渡期准备中自我管理支持的证据总结

目的:总结癫痫青少年至成人过渡期准备中自我管理支持的相关证据,为临床医护人员及管理决策者提供循证依据。方法:计算机检索关于癫痫青少年至成人过渡期准备中自我管理支持的指南、最佳实践、推荐实践、系统评价、证据总结、专家共识等,检索时限为从建库至2023年5月10日。进行方法学质量评价后,结合专业人员的判断,进行证据提取与汇总。结果:根据纳入标准共筛出19篇文献,包括5篇指南、6篇系统评价、7篇专家共识和1篇推荐实践。从环境支持(共同参与、平台建设)、行为支持(发展过渡期档案、提高服用抗癫痫药物依从性的技能、优化生活方式的技能、院外急性癫痫发作应急技能)和认知支持(患者教育、正性心理建设、共享决策发展)3个方面,9个类别汇总了34条证据。结论:本研究总结的癫痫青少年至成人过渡期准备中自我管理支持的证据具有一定的全面性、科学性和实用性,为慢性病青少年自我管理支持的实践规范提供依据。

血液透析患者移植物动静脉内瘘并发症预防与管理的证据总结

目的 :检索、评价及总结维持性血液透析患者移植物动静脉内瘘并发症预防与管理的相关证据,为临床减少血液透析患者移植物动静脉内瘘相关并发症的发生提供循证依据。方法 :系统检索UpToDate、BMJ最佳临床实践、澳大利亚乔安娜布里格斯研究所(JBI)循证卫生保健中心、美国指南网、苏格兰学院间指南网站、PubMed、中国知网、中华医学期刊全文数据库、医脉通、万方数据库、维普数据库等与血液透析患者移植物动静脉内瘘并发症预防与管理相关的文献,包括临床决策、证据总结、指南、专家共识、系统评价、随机对照试验等,检索时限为建库至2023年6月30日。由专业循证护理人员对文献进行质量评价及证据提取、汇总。结果 :共纳入15篇文献,包括临床决策7篇、指南5篇、专家共识1篇、队列研究1篇、随机对照试验1篇,结合临床专业人员意见汇总了5个主题,包括围手术期管理、监测与筛查、使用与维护、人员与培训及健康教育,共33条证据。结论 :该研究总结的维持性血液透析患者移植物动静脉内瘘并发症预防与管理的证据具有全面性和科学性,为临床医护人员规范移植物动静脉内瘘的管理提供循证依据。

成人PICC/上臂输液港原发性导管异位管理的最佳证据总结

目的评价、总结国内外成人PICC/上臂输液港原发性导管异位管理的最佳证据,为临床医护人员识别、预防和处理成人PICC/上臂输液港原发性导管异位提供参考。方法系统检索中英文数据库、指南相关网站和专业学会网站中有关成人PICC/上臂输液港原发性导管异位管理的证据,包括临床决策、最佳实践、临床实践指南、系统评价、证据总结和专家共识。检索时限为建库至2023年12月31日。2名研究者独立对纳入文献进行质量评价,并结合专业判断进行标准化的资料提取和证据分级。结果该研究共纳入13篇文献,包括6篇临床实践指南、2篇证据总结和5篇专家共识。最终从识别、预防及处理3个方面总结了39条最佳证据。结论该研究为临床医护人员管理成人PICC/上臂输液港原发性导管异位提供循证依据。在临床应用时,需要充分考虑临床情景,结合专业人士的判断及患者的意愿,遵循个体化原则,审慎地将证据应用于临床实践中。

成人患者安全输注血管活性药物的最佳证据总结

目的总结成人患者安全输注血管活性药物的最佳证据总结,为临床护理工作提供证据支持。方法检索UpToDate、BMJ Best Practice、澳大利亚乔安娜布里格斯研究所循证卫生保健中心数据库、Cochrane Library、美国国立指南库、美国疾病预防与控制中心、英国国家临床医学研究所指南库、国际指南协作网、CINAHL、Embase、加拿大安大略注册护士协会、英国重症监护协会、美国静脉输液护理学会、PubMed、Web of Science、中国知网、万方数据库、维普网、中华护理学会、中国生物医学文献数据库和医脉通中有关成人患者血管活性药物输注的指南、专家共识、团体标准、系统评价、证据总结和原始研究等证据。检索时限为2013年1月1日-2023年3月10日。由2名具有循证护理研究背景的研究者独立进行文献质量评价及证据筛查,证据汇总后由团队归类综合。结果共纳入12篇文献,从培训与教育、评估、血管通路选择、输注方案、并发症管理5个维度总结了29条证据。结论该研究总结了成人患者血管活性药物输注的最佳证据,可为医务人员提供科学的输注方案,保证患者安全,提高护理质量。

眼科超声乳化手柄清洗质量管理的最佳证据总结

目的检索、评价并整合国内外眼科超声乳化手柄清洗质量管理的相关证据,为临床实践提供指导。方法计算机检索国际指南协作网、美国国立指南网、BMJ Best Practice、PubMed、万方数据库、中国知网等网站或数据库中关于眼科超声乳化手柄清洗质量管理的所有证据,包括指南、专家共识、系统评价及证据总结,检索时限为从建库至2023年2月15日。结果共纳入10篇文献,其中实践指南4篇,专家共识4篇,系统评价1篇,证据总结1篇。总结了管理要求、环境要求、设备设施与耗材要求、人员要求、操作要求、现场预处理、回收转运、清洗及清洗质量评价9个方面共38条证据。结论该研究总结了眼科超声乳化手柄清洗质量管理的最佳证据,为规范超声乳化手柄的清洗质量管理提供了循证依据,可为提高眼科器械清洗质量水平、预防与控制医院感染发生提供参考。

“新工科”背景下高校课程思政实施探索与总结

“新工科”建设是新时代我国工程教育发展的新思维、新格局、新走向。培养具有时代蕴涵和中国特色的“新工科”人才,需要构建高水平人才培养体系,其中课程思政建设不可或缺。我校工学院在顺应发展的过程中,积极开展课程思政的过程中所采取的一些措施。本文论述了这些措施是如何有力保证课程思政的选材、设计和实施的。且对在两个学期的专业课程思政改革中所取得的一些思想上、行为上的积极探索和实践进行必要的反思和总结,并希望由此可以设计或演化出一套具体可行的课程思政方法,在全校中进行普及与应用。

基于最佳证据总结的康复护理在脑卒中偏瘫患者中的应用

目的 本研究探讨基于最佳证据总结的康复护理方案在脑卒中偏瘫患者中的应用,并进行分析。方法 选取医院2022年1—12月收治的脑卒中偏瘫患者120例,按照组间基线资料均衡可比的原则分为对照组和观察组,每组60例。对照组给予常规康复护理,观察组在常规康复护理基础上给予基于最佳证据总结的康复护理。比较两组患者干预前后神经功能缺损程度、肌张力、运动功能、日常生活能力和负性情绪。结果 干预前,两组患者的神经功能缺损程度(NIHSS)评分、肌张力(Ashwoth)评分、上肢运动功能(UL-FMA)评分、下肢运动功能(FMA-L)评分、日常生活能力(MBI)评分、焦虑情绪(HAMA)评分、抑郁情绪(HAMD)评分比较差异均无统计学意义(P>0.05)。干预后,观察组患者神经缺损程度的评分低于对照组,差异有统计学意义(P<0.05)。干预后,观察组患者肌张力和运动功能评分优于对照组,差异有统计学意义(P<0.05)。干预后,观察组日常生活能力评分高于对照组,差异有统计学意义(P<0.05)。干预后,观察组焦虑、抑郁评分低于对照组,差异有统计学意义(P<0.05)。结论 基于最佳证据总结的康复护理可减轻脑卒中偏瘫患者神经功能缺损程度,改善肌张力,提升运动功能和日常生活能力,并可缓解负性情绪,促进身心康复。

肿瘤患儿化疗相关性口腔黏膜炎护理的最佳证据总结

目的:检索、评价并总结肿瘤患儿化疗相关性口腔黏膜炎护理的最佳证据总结,为医护人员临床工作提供借鉴。方法:依据“6S”证据模型自上而下系统检索相关指南网站、专业学会网站及国内外数据库中关于肿瘤患儿化疗相关性口腔黏膜炎护理的临床决策、指南、专家共识、系统评价、证据总结、原始文献。检索时限为2013年6月—2023年6月。由2名研究者独立进行文献质量评价、证据提取及整合。结果:最终纳入11篇文献,其中指南6篇、专家共识1篇、系统评价2篇、随机对照试验2篇。汇总了包括评估、健康教育、基础口腔护理、冷冻疗法、光生物调节疗法、药物及其他方法6个方面共23条最佳证据。结论:本研究总结了肿瘤患儿化疗相关性口腔黏膜炎护理的最佳证据,建议医护人员应用证据时结合临床情境及实际情况,选择性应用证据。

敦煌论坛:敦煌学研究弘扬的世界意义学术研讨会综述

2023年9月5日-8日,由甘肃省委宣传部、甘肃省文物局主办,敦煌研究院承办的“敦煌论坛:‘敦煌学研究弘扬的世界意义’学术研讨会”在敦煌举行。来自中国、俄罗斯、美国、英国、法国、日本、意大利、土耳其、韩国、伊朗等11个国家和地区的百余名专家学者相聚敦煌,共同探讨国际视野下的敦煌学研究与敦煌文化艺术传承发展。

脑卒中病人早期吞咽障碍评估与管理指南的证据总结

目的:检索、评价和分析脑卒中病人早期吞咽障碍评估与管理指南的最佳证据,为规范我国急性脑卒中后吞咽障碍病人的管理提供借鉴。方法:计算机检索各大指南网、数据库和专业协会网站中关于脑卒中病人早期吞咽障碍评估与管理的指南。由3名研究员采用临床指南研究和评价系统Ⅱ对其质量进行评价,并进行资料提取。结果:共纳入指南14篇,进行归纳和分析后,最终形成组织与培训、筛查与评估、吞咽障碍管理、辅助治疗和健康教育5个方面共31条最佳证据。结论:本研究总结的脑卒中病人早期吞咽障碍评估与管理的最佳证据,为我国医疗机构制定相应的评估流程和管理方案提供借鉴。护理人员应结合临床情境、病人意愿,审慎地选择并应用证据,从而提高脑卒中病人早期吞咽障碍评估与干预水平,改善病人的营养状态和预后。