Trends of infodemiology studies: a scoping review

Introduction

The health care industry is rich in data and information. Web technologies, such as search engines and social media, have provided an opportunity for the management of user generated data in real time in the form of infodemiology studies. The aim of this study was to investigate infodemiology studies conducted during 2002–2016, and compare them based on developed, developing and in transition countries.

Methods

This scoping review was conducted in 2017 with the help of the PRISMA guidelines. PubMed, Scopus, Science Direct, Web of Knowledge, Google Scholar, Wiley and Springer databases were searched between the years 2002 and 2016. Finally, 56 articles were included in the review and analysed.

Results

The initial infodemiology studies pertain to the quality assessment of the hospital's websites. Most of the studies were on developed countries, based on flu, and published in the Journal of Medical Internet Research.

Conclusion

The infodemiology approach provides unmatched opportunities for the management of health data and information generated by the users. Using this potential will provide unique opportunities for the health information need assessment in real time by health librarians and thereby provide evidence based health information to the people.

     

Spatial Discovery of Linked Research Datasets and Documents at a Spatially Enabled Research Library

Current publishing practices in academia tend to result in datasets that are difficult to discover. This is because datasets are not well-integrated across academic domains and they are often not linked to the documents that reference them. For these reasons, discovering datasets across domains can be challenging; for example, discovering archeological observations and biological specimens using the same search is not widely supported, even if both datasets share a similar spatial extent, like Mesoamerica. It is also challenging to retrieve relevant documents that reference datasets; for example, retrieving a series of field reports that reference archeological observations is typically not supported. Our work develops an extensible method for: (1) geographically integrating collections across disciplinary repositories and (2) connecting datasets to related documents. We describe a collection of spatially-referenced researcher datasets, capturing their metadata elements and encoding them as linked open data. We then leverage existing library services to formalize links from datasets to documents. The system described in this work has been deployed, resulting in an experimental open data site for the UCSB campus. Results indicate that this system can be scaled-up with support from an institutional repository in the near future.     

An impulse to exploit: the behavioral turn in data-driven marketing

Industry advocates argue that by tailoring services and commercial solicitations to match media users’ personal interests, data-driven marketing benefits both consumers and businesses. This article shows, however, that advertisers and marketers who are taking up ideas and techniques from behavioral economics tell their clients a very different story about the aims and use of digital marketing and consumer surveillance. Listening in on this discourse demonstrates that some digital marketers conceptualize their own practices as forms of social control, appropriating concepts from behavioral economics to identify consumers’ cognitive and affective biases and target their vulnerabilities. Behavioral economics recognizes that economic decisions are not simply dictated by rational self-interest; rather, such choices depend on cognitive heuristics and habits, and can be manipulated through the design of “choice architecture.” This article discusses implications of the behavioral turn in data-driven marketing for critical advertising scholars, public advocates, and regulators.     

Multimodal data as a means to understand the learning experience

Most work in the design of learning technology uses click-streams as their primary data source for modelling & predicting learning behaviour. In this paper we set out to quantify what, if any, advantages do physiological sensing techniques provide for the design of learning technologies. We conducted a lab study with 251 game sessions and 17 users focusing on skill development (i.e., user's ability to master complex tasks). We collected click-stream data, as well as eye-tracking, electroencephalography (EEG), video, and wristband data during the experiment. Our analysis shows that traditional click-stream models achieve 39% error rate in predicting learning performance (and 18% when we perform feature selection), while for fused multimodal the error drops up to 6%. Our work highlights the limitations of standalone click-stream models, and quantifies the expected benefits of using a variety of multimodal data coming from physiological sensing. Our findings help shape the future of learning technology research by pointing out the substantial benefits of physiological sensing.     

中文电子病历的分词及实体识别研究

[目的/意义]健康医疗大数据是我国重要的基础性战略资源，本研究对中文电子病历分词与实体识别的探讨与实证较好地完成了医疗数据的信息抽取任务，对今后医疗大数据在语义层面的应用发展具有重要意义。[方法/过程]本研究首先融合权威词表、官方标准、健康网站数据及其他医学补充词库构建了词语数量级达到10万的医学词表；然后对电子病历的字段进行分词，对比了jieba工具、导入词典后的jieba、无监督学习及AC自动机4种模型的分词效果；最后，以自动分词和人工标注结果为语料，实现基于条件随机场的电子病历实体识别研究，并比较不同实体类别以及不同文本特征下的实体识别效果，选出最优模板。[结果/结论]分词结果显示，AC自动机的效果最好，F值可达82%；实体识别结果表明，"检查"和"疾病"实体的识别效果最好，而"症状"的识别效果不太理想。     

情报学取向的“数据科学与大数据技术”专业人才培养

[目的/意义]情报学作为计算性较强的人文社科学科，具备数据科学的特点，大数据相关技术能为情报学提供有效手段，因此，设立情报学取向的"数据科学与大数据技术"专业，培养具有大数据思维和扎实的情报学专业基础、掌握大数据处理技术及分析理论、并能将其应用于情报学理论与实践的复合型人才，能够促进情报学学科发展。[方法/过程]对国内外大数据相关专业的培养目标和课程设置模式进行调研，总结学科教育发展特色，结合国内的教学资源情况，设计情报学取向的大数据专业人才培养计划。[结果/结论]情报学与大数据既有联系又有不同，情报学为大数据提供了基础研究方法，促进了多源数据融合，拓宽了大数据的应用领域；大数据为情报学研究提供了更多的可能性、丰富了研究方法，提升了研究效率、扩大了研究范围。情报学取向的大数据与数据科学专业人才的培养，既有其必要性，又有其不可替代性。学科体系需进一步改革创新，设立更多紧密结合大数据与情报学专业的新兴课程，如文献大数据分析、用户大数据研究等，系统地培养学生将情报学领域知识与计算机技术和大数据技术融合创新的能力。     

基于野外测量生成GIS数据方法分析

地理信息系统的核心,就是获取数据。搜集精准数据,才能描画完备的测量图。为满足测量需求,应不断深入解析,提供实时特性的精准数据。获取的GIS数据,被用在野外范畴的测量之中。确认数据生成特有的路径,提升GIS测定的精准性,便利后续的导入数据。     

我国图书情报领域期刊论文的科学数据引用特征研究

[目的/意义] 探索期刊论文科学数据引用特征与规律不仅有助于描述学科领域对科学数据的利用情况，还能够揭示学术成果表达中的数据引用模式。[方法/过程] 以我国图书情报领域6种期刊2017年与2018年第一期刊载论文为样本，结合国家标准《信息技术科学数据引用》的引用元素，采用内容分析法从9个维度对样本论文的科学数据引用行为进行数据编码，应用统计学方法描述图书情报领域期刊论文科学数据引用特征并探索不同维度特征间的关联关系。[结果/结论] 图书情报领域期刊论文广泛引用来自国内外的统计整理类科学数据，对期刊论文中个人研究科学数据的引用量较大；科学数据引用标注方式与科学数据类型存在一定对应关系，但多样化的标注方式缺乏统一性；二手引用现象较为突出，二手引用程度与科学数据创建者类型相关。