A deep learning-based multi-turn conversation modeling for diagnostic Q&A document recommendation

Online healthcare communities (OHCs) have become producers of medical information. Solving the issue of how to effectively reuse such a large amount of medical data and discover its potential value is of the utmost importance for alleviating the shortage of medical resources. Online consultation has received widespread attention and population since its first appearance in 1999, and as a result, many diagnostic multi-turn questions and answers (Q&A) documents have become available. This type of document is formed by multiple rounds of patient questions and doctors’ diagnostic answers and contains massive medical knowledge and doctors’ diagnostic experience. Few studies concentrate on the modeling and recommendation of this type of document, yet making these documents convenient for reuse reduces the cost of medical consultation for patients and saves time addressing common diseases for doctors. In this paper, we focus on the modeling and understanding of diagnostic multi-turn Q&A records and propose a deep-learning recommendation framework based on patient medical information needs, the contents of Q&A records and doctor background information. With the evaluation based on a real dataset that contains pediatric consultation dialogues fetched from DingXiangYuan, a famous online consultation application in China, we found that the proposed model achieved a good performance on the recommendation of diagnostic multi-turn Q&A records and outperformed baseline models. In addition, we discussed a potential application scenario of the recommendation model, suggesting that the proposed model can promote the reduction of patient costs and doctors’ work pressure in countries or regions with insufficient medical resources.     

Some critical episodes in the progress of medical innovation: An Anglo-American perspective

The central concern of this paper is to show that medical innovations have depended heavily on breaking down barriers that have long prevailed in the academic world, in the form of disciplinary boundaries that have coalesced into separate departments; to be specific, some of the biggest breakthroughs for the Life Sciences have come from the realm of the Physical Sciences. The present study is confined mainly to molecular biology and to diagnostic technologies (as well as to the therapeutic technologies that have frequently flowed from them); both owed a great deal to institutional innovations that emerged in the Anglo-American medical research world. Opportunities for transfers of instrumentation and techniques across disciplinary boundaries have been considerably strengthened as medical schools have been located, geographically and organizationally, closer to the universities. The American Medical Centers and the Stanford Program provide many examples. These achieved more than counterparts in the UK like the Cavendish Laboratories at Cambridge, which had pioneered in such fields.     

E-government information search by English-as-a Second Language speakers: The effects of language proficiency and document reading level

A rapid increase in the use of web-based technologies – and corresponding changes in government and local council policies – in recent years, means that many vital services are now provided solely online. While this has many potential benefits, it can place additional burdens on certain demographic groups, some of whom may become considerably disadvantaged or even disenfranchised. This is particularly problematic for English-as-a Second Language (ESL) speakers, who are often immigrants or refugees and thus have a greater need to access these e-government services, and who may struggle to understand and assess the relevance of complex documents. In this work we investigate the search behaviours and performance of native English speakers and two different groups of ESL speakers when completing e-government tasks, and the effect of document readability/complexity. In contrast with previous work, our results show significant differences between groups of varying language proficiency in terms of objective search performance, time on task, and self-perceived performance and confidence. We also demonstrate that document reading level moderates the effect of language proficiency on objective search performance. The findings contribute to our existing understanding of how English language proficiency affects search for e-government topics, and have important implications for the future development of e-government services to ensure more equitable access and use.     

Network text analysis: A two-way classification approach

Text clustering is a well-known method for information retrieval and numerous methods for classifying words, documents or both together have been proposed. Frequently, textual data are encoded using vector models so the corpus is transformed in to a matrix of terms by documents; using this representation text clustering generates groups of similar objects on the basis of the presence/absence of the words in the documents. An alternative way to work on texts is to represent them as a network where nodes are entities connected by the presence and distribution of the words in the documents. In this work, after summarising the state of the art of text clustering we will present a new network approach to textual data. We undertake text co-clustering using methods developed for social network analysis. Several experimental results will be presented to demonstrate the validity of the approach and the advantages of this technique compared to existing methods.     

治理网络视角下新兴技术治理困境和消解因素——————来自中国转基因作物治理的经验

新兴技术治理无法回避的是如何应对新兴技术的不确定性,而既定的精英决策模式不仅难以应对新兴技术不确定性带来的社会问题,而且制定的新兴技术政策受到了广泛社会公众的强烈质疑和争议。网络制度下,通过多元行动者协商和互动制定的新兴技术政策消减新兴技术不确定性成为新兴技术治理的关键。研究将治理网络理论作为一个经验工具来分析中国转基因作物治理应对不确定性的政策博弈过程,研究结果表明政府、市场、社会公众之间经过协商和互动的治理行动是应对新兴技术不确定性的消解因素。研究启示是新兴技术政策制定需要社会价值前置,是过程导向治理的体现,也是社会公众权利的回归。     

医疗康复机器人研究进展及趋势

随着人们对医疗健康手段和过程提出的精准、微创、高效及低成本等方面的更高需求,医疗康复机器人技术也获得了各国的极大关注,并得到了日新月异的发展。目前医疗康复机器人主要用于外科手术、功能康复及辅助护理等方面,但随着重要技术的突破和进展,未来机器人技术有可能会应用到医疗健康的各个领域。医疗康复领域越来越倾向于人与机器自然、精准的交互,近年来,以人的智能和机器智能结合及人机交互为代表的技术突破使得人与机器之间的结合越来越紧密,借助人机交互技术和方法,将人的智能和机器智能结合起来,使二者优势互补、协同工作,并将在医疗康复方面孕育出重大的理论创新和技术方法突破。社会需求、技术革新和人机智能融合极大的促进了医疗康复机器人的发展。医疗康复机器人涉及人类生命健康的特殊领域,存在潜在的经济市场,已被多个国家列为战略性新兴产业,我国也需进一步大力开展医疗康复机器人的研发,推动该战略新兴产业的发展,以应对我国国民对健康服务的需求(医疗、康复及老龄化)。     

Uncertainty and the inconvenient facts of diagnosis

One common contemporary usage of the term “diagnostic uncertainty” is to refer to cases for which a diagnosis is not, or cannot, be applied to the presenting case. This is a paradoxical usage, as the absence of diagnosis is often as close to a certainty as can be a human judgement. What makes this sociologically interesting is that it represents an “epistemic defence,” or a means of accounting for a failure of medicine’s explanatory system. This system is based on diagnosis, or the classification of individual complaints into recognizable diagnostic categories. Diagnosis is pivotal to medicine’s epistemic setting, for it purports to explain illness via diagnosis, and yet is not always able to do so. This essay reviews this paradoxical use, and juxtaposes it to historical explanations for non-diagnosable illnesses. It demonstrates how representing non-diagnosis as uncertainty protects the epistemic setting by positioning the failure to locate a diagnosis in the individual, rather than in the medical paradigm.     

从世界顶级学术期刊看中国科研竞争力——中国学者《自然》和《科学》发文分析

利用文献计量学方法,对WoS数据库中Nature和Science刊载的中国学者主持或参与的论文从各年文献量、文献类型、核心作者、合作国家与机构、被引频次等多方面进行了分析,力图深入剖析中国学者科研成果的水平以及国际影响力,最后根据分析结果对如何提升中国学者的国际竞争力提出了建议.     

Education of medical biochemists in Bosnia and Herzegovina

In this paper we would like to briefly introduce readers to the situation in the field of laboratory medicine in Bosnia and Herzegovina, with a focus on training in the field of medical biochemistry. As in some of neighboring countries, term Medical biochemist is the usual name for the Clinical biochemist or Clinical chemist in Bosnia and Herzegovina. Despite the difficult period through which the profession had passed in the last two decades, laboratory work, particularly clinical biochemistry, has managed to retain the necessary quality and keep pace with the developed world. In post war period, Society of Medical Biochemists of Bosnia and Herzegovina held regular meetings each year as a part of "life long learning" process, where both scientific and vocational lecturers presented their work. A single law on the state level would provide us with more defined and precise answers, such as: who can get a specialization, how long should last the training for medical biochemistry specialists (duration in years). This law should be in consent with the program described in EC4 or other documents given by the EFCC (European Federation of Clinical Chemistry and Laboratory Medicine) and IFCC (International Federation of Clinical Chemistry and Laboratory Medicine).     

The impact of geographical distance on learning through collaboration

There is little question that new communication and transportation technologies have effectively “shrunk the world” for a great many activities. At the same time, the “death of distance” has been greatly exaggerated, especially in fields such as academic scholarship and knowledge development where the positive benefits of knowledge spillovers remain highly distance dependent. We analyze 17.6 million publications authored by 1.7 million scholars to examine how knowledge spillovers between scholars collaborating at different geographical distances impacts their future knowledge portfolios. Our results show that in 1975, scholars collaborating locally were 57 % more likely to learn from knowledge spillovers than similar scholars collaborating non-locally. We identify four factors that structure these findings. Individuals deriving the greatest learning premiums from local collaboration tend to be (1) in earlier stages of their career; (2) associated with lower-ranked institutions; (3) working with fewer collaborators; and (4) in STEM fields. The probability of learning drops with geographical distance and correspond to the number of institutional boundaries crossed during collaboration. We conclude that even in the 21st century, geographical distance still negatively impacts knowledge spillovers through collaboration. These findings have implications for debates in innovation and management studies concerning knowledge spillovers, the spatial organization of (knowledge-intensive) economic activity, regional innovation policies, structuring team-work and working-from-home vs. returning to office.     

Selecting optimal training data for learning to rank

This paper is concerned with the quality of training data in learning to rank for information retrieval. While many data selection techniques have been proposed to improve the quality of training data for classification, the study on the same issue for ranking appears to be insufficient. As pointed out in this paper, it is inappropriate to extend technologies for classification to ranking, and the development of novel technologies is sorely needed. In this paper, we study the development of such technologies. To begin with, we propose the concept of “pairwise preference consistency” (PPC) to describe the quality of a training data collection from the ranking point of view. PPC takes into consideration the ordinal relationship between documents as well as the hierarchical structure on queries and documents, which are both unique properties of ranking. Then we select a subset of the original training documents, by maximizing the PPC of the selected subset. We further propose an efficient solution to the maximization problem. Empirical results on the LETOR benchmark datasets and a web search engine dataset show that with the subset of training data selected by our approach, the performance of the learned ranking model can be significantly improved.     

Bottom-up estimation of industrial and public R&D investment by technology in support of policy-making: The case of selected low-carbon energy technologies

This paper discusses a bottom-up approach to estimate the level of R&D investment by technology in areas where data are scarce. It develops a four-step methodology for the estimation of corporate R&D investments at technology level. This approach can overcome gaps in existing data by combining publicly available information in a novel way, even though it introduces some uncertainty. This is illustrated for a set of low-carbon energy technologies that were identified as key for meeting Europe's long-term energy and climate objectives by the European Strategic Energy Technology Plan. The paper finds that the aggregated R&D investments dedicated to these technologies amounted to €3.3 billion in the EU in 2007, including public funding from European Union Member States and at EU-level, and industrial research activities from companies with headquarters registered in the EU. The results allow conclusions on the European energy research policy to be drawn, such as the dominance of industrial funds, and have provided significant input to the European policy making in this field. The paper ends with suggestions on how to further enhance the accuracy of the approach and how to widen its application to other sectors.     

新兴技术实践伦理的困境与应对

为应对来自技术的伦理问题,国内外学者提出了多种实践伦理方法。新兴技术作为尚未投产及仍处研发阶段的一系列尖端技术的总和,其自身具有不确定性和巨大影响力这两大特点。因此由新兴技术所带来的伦理问题相较一般技术而言也有其独特特征,并由此在实践伦理过程中产生了相应的困境,其特点主要为后果更难以预测、范围和程度更大、更为复杂多元,并且较难得到公众理解。为应对新兴技术实践伦理的困境应将研究对象从新兴技术转向新兴技术实践过程,把问题还原到实践本身,通过对实践场域及实践主体行动逻辑的认识弥补实证性经验材料的不足。新兴技术实践伦理从研发、投产、使用、治理四个实践场域分别入手,在每个实践场域中靶向性地匹配伦理原则、理论方法。对研发设计进行调解,优化投产及使用过程,并在技术治理中寻求解决路径。     

Learning entity-centric document representations using an entity facet topic model

Learning semantic representations of documents is essential for various downstream applications, including text classification and information retrieval. Entities, as important sources of information, have been playing a crucial role in assisting latent representations of documents. In this work, we hypothesize that entities are not monolithic concepts; instead they have multiple aspects, and different documents may be discussing different aspects of a given entity. Given that, we argue that from an entity-centric point of view, a document related to multiple entities shall be (a) represented differently for different entities (multiple entity-centric representations), and (b) each entity-centric representation should reflect the specific aspects of the entity discussed in the document.In this work, we devise the following research questions: (1) Can we confirm that entities have multiple aspects, with different aspects reflected in different documents, (2) can we learn a representation of entity aspects from a collection of documents, and a representation of document based on the multiple entities and their aspects as reflected in the documents, (3) does this novel representation improves algorithm performance in downstream applications, and (4) what is a reasonable number of aspects per entity? To answer these questions we model each entity using multiple aspects (entity facets¹), where each entity facet is represented as a mixture of latent topics. Then, given a document associated with multiple entities, we assume multiple entity-centric representations, where each entity-centric representation is a mixture of entity facets for each entity. Finally, a novel graphical model, the Entity Facet Topic Model (EFTM), is proposed in order to learn entity-centric document representations, entity facets, and latent topics.Through experimentation we confirm that (1) entities are multi-faceted concepts which we can model and learn, (2) a multi-faceted entity-centric modeling of documents can lead to effective representations, which (3) can have an impact in downstream application, and (4) considering a small number of facets is effective enough. In particular, we visualize entity facets within a set of documents, and demonstrate that indeed different sets of documents reflect different facets of entities. Further, we demonstrate that the proposed entity facet topic model generates better document representations in terms of perplexity, compared to state-of-the-art document representation methods. Moreover, we show that the proposed model outperforms baseline methods in the application of multi-label classification. Finally, we study the impact of EFTM’s parameters and find that a small number of facets better captures entity specific topics, which confirms the intuition that on average an entity has a small number of facets reflected in documents.     

我国海外科研人才的需求分析及人才引进对策

当前我国正处于海外人才引进的战略机遇期,国际贸易战争、科技战争的形势也使人才引进工作刻不容缓,而探知海外人才的工作需求,是人才引进工作的前提和基础。本文以新加坡为例,通过对我国在新加坡的科研人才进行问卷调研,并对调研数据进行因子分析,探究我国海外科研人才的工作动机和需求。研究发现不同阶段的科研人才对工作和生活环境的关注点不同。事业起步阶段的青年学者更注重科研工作的责任、挑战、发展和成长等科研激励因素,资深学者对工作的稳定、安全和薪酬待遇等工作保健因素更为关注,同时发现个人、家庭的生活保障和文化旅游因素也在一定程度上受到学者的关注。在此基础上,本文建议我国的海外人才政策不应该套用一个模式,而是应该针对不同的目标人群采用不同的工作重点,制定灵活的人才政策。     

三十余年图情领域重要期刊文献量化分析

利用Histcite、SPSS和Citespace等工具,对Web of science数据库收录的图情领域重要期刊进行量化分析,研究发现美国和英国为其重要文献来源国,我国学者研究文献被收录数量相对较少但却具有较高的学术水准和参考价值.同时发现图情领域研究发展历程中始终围绕图书馆和信息科学两大主题展开研究,经历了传统研究方法的应用——方法理论改善——新评价手段提出——新评价手段体系构建和完善的发展历程,且逐渐从满足社会需求和推动科学进步的角度,挖掘并提升图情研究的存在价值和意义.     

组织知识结构构建方法及在专利文献中的运用

组织知识结构是组织中知识的基本构成及其相互关系的反映,但目前学者们较少关注组织知识结构构建方法的研究。针对这一问题,本文首先从宏观和微观两个层面明确组织知识结构的概念,然后以物质载体中的专利文献为例,使用文本挖掘的方法对组织知识结构的构建方法进行研究。该方法首先抽取专利文献中的知识元,进而采用层次凝聚聚类算法(HAC)对这些知识元进行层次聚类,并基于可视化方法形成组织知识结构。最后,以某组织的专利文献背景对其组织知识结构构建方法进行了实例验证,取得了良好的效果。     

Recommended for “frequent perusal” and “improving the science of medicine”: Benjamin Rush’s American editions and the circulation of medical knowledge in the early Republic

Between 1809 and 1813 leading American physician Benjamin Rush (1745–1813) devoted a significant portion of his time to the production of “American Editions” of four British and colonial medical texts by Thomas Sydenham (1624–1689), Sir John Pringle (1707–1782), William Hillary (1697–1763), and George Cleghorn (1716–1789). This occurred during a period where Rush might have written a textbook detailing his preferred medical system for students outright. Instead, he opted for a different form of knowledge production and proliferation that focused on creating fictive conversations and encouraging critical reading rather than rote memorization. He dedicated these heavily annotated documents to the medical students of the United States of America and set them up as a pedagogical tool. Analysis of these texts helps uncover the reading practices of Rush and the manner in which he expected Americans to mediate their usage of foreign texts and theories after the American Revolution.     

Of frogs and men: the origins of psychophysiological time experiments, 1850-1865

Towards the end of the 1840s, Hermann von Helmholtz began to investigate experimentally the propagation of stimuli within nerves. Helmholtz's experiments on animals and human subjects opened a research field that in the following decades was intensively explored by neurophysiologists and experimental psychologists. Helmholtz's pioneering investigations justify the central place he occupies in accounts of the history of modern psychophysiology. Studying the concrete experimental settings and their local contexts shows how deeply the work of scholars such as Helmholtz is embedded in the history of culture and technology. In particular, the rapidly growing technologies of electromagnetism, which gave rise to telegraphy and electric clocks, facilitated the time measurements of 19th-century physiologists and psychologists.     

Old Freedoms and New Technologies: The Evolution of Community Networking

Unparalleled in human experience, the Internet, or simply the Net, is the code word for the technosocial accident that gives large numbers of people the means by which they can speak for themselves in public. This is an ironical reversal of the historical social patterning of asymmetrical, centralizing communicating technologies that have molded all of the social relations of modern society. The problematic for this distributed communication capability will be manifest in struggles around the legitimacy of self-expression, assembly, and privacy, in all of their forms. However, unlike the mass mediated discourse where, as the 'audience' object, we observed these externalized struggles by a narrow other, encounters with distributed media are palpable and subjective, and will be increasingly played out on the common terrain of local community. In initiating unconditional public access to the Net, community networks, or FreeNets, began the long process of blurring the distinction between the public and private terrain, of undoing that dichotomy that mass media technologies in this century have systematically rebuilt and fortified. Nudging along the process of democratic self-representation is the central issue for the Net, and the epochal project for community networks.