Special Focus on Biomedical Data Science

L. Ohno-Machado
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引用次数: 2

Abstract

JAMIA has documented the evolution of biomedical informatics through its dissemination of original research and applications, brief communications and case studies, thought-provoking perspectives, and insightful reviews. The number and diversity of data-driven models have increased substantially in the past few years. From the first developments in machine and statistical learning that were applied to health sciences decades ago, our field has flourished to include biomedical data science as one of its important components, which is possible only because of other informatics work that allows data to be standardized, integrated, and used in various learning models. This issue is focused on biomedical data science and illustrates a broad range of techniques and application areas in this field; articles submitted in response to a specific request for papers are featured in an editorial by Brennan et al. (p. 2). In addition to the articles covered in the editorial, this issue highlights tools and applications of data science in a variety of domains, all of which use clinical text as a source of data: Trivedi (p. 81) presents an interactive tool for processing clinical text, Luo (p. 93) uses convolutional neural networks to classify relations in clinical notes, and Bejan (p. 61) introduces an approach to find homelessness and adverse childhood experiences described in clinical narratives. Additionally, nonclinical text is increasing in importance for health care and public health. Xie (p. 72) uses recurrent neural networks to find e-cigarette adverse events in social media posts, while Vigo (p. 88) describes a method to collect seasonal allergy symptoms for the British population. New types of structured data and new ways to integrate them are also continuously being produced: Doostparasti (p. 99) describes a novel approach for integrating -omics data to enhance phenotype classification performance, and Yu (p. 54) introduces a phenotyping algorithm that does not depend on expert-labeled observations. The articles listed above are only a few examples of the scope of informatics activities covered in JAMIA. Starting with this January issue, readers will be able to easily group articles into themes based on technologies used or application areas. This grouping is made possible by JAMIA’s change in frequency and format (to monthly online), which will allow for more frequent indexing. Readers will be able to compare approaches and discover solutions that are best suited to their problems. Stay tuned for additional data science articles in future monthly issues, as well as articles focused on clinical informatics systems (including clinical decision support), clinical research systems, translational bioinformatics, global public health informatics, and many other subfields of informatics that help us, through information technology, understand and address human health and disease.
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特别关注生物医学数据科学
JAMIA通过传播原始研究和应用、简短的交流和案例研究、发人深省的观点和有见地的评论,记录了生物医学信息学的发展。在过去几年中,数据驱动模型的数量和多样性大大增加。从几十年前应用于健康科学的机器和统计学习的第一次发展开始,我们的领域已经蓬勃发展,将生物医学数据科学作为其重要组成部分之一,这是可能的,因为其他信息学工作允许数据标准化,集成并用于各种学习模型。本刊重点关注生物医学数据科学,并阐述了该领域的广泛技术和应用领域;响应特定论文请求而提交的文章在Brennan等人的社论中有特色(第2页)。除了社论中涵盖的文章外,本期还重点介绍了数据科学在各个领域的工具和应用,所有这些领域都使用临床文本作为数据来源:Trivedi(第81页)提出了一种用于处理临床文本的交互式工具,Luo(第93页)使用卷积神经网络对临床记录中的关系进行分类,Bejan(第61页)介绍了一种方法来发现临床叙述中描述的无家可归和不良童年经历。此外,非临床文本对卫生保健和公共卫生的重要性日益增加。Xie(第72页)使用递归神经网络在社交媒体帖子中发现电子烟不良事件,而Vigo(第88页)描述了一种收集英国人群季节性过敏症状的方法。新类型的结构化数据和整合它们的新方法也在不断产生:Doostparasti(第99页)描述了一种整合组学数据以增强表型分类性能的新方法,Yu(第54页)介绍了一种不依赖于专家标记观察的表型算法。上面列出的文章只是JAMIA所涵盖的信息学活动范围的几个例子。从今年1月开始,读者将能够根据所使用的技术或应用领域轻松地将文章分组为主题。这种分组是由于JAMIA在频率和格式上的改变(改为每月在线),这将允许更频繁的索引。读者将能够比较方法并发现最适合他们问题的解决方案。请继续关注未来月刊中更多的数据科学文章,以及关注临床信息学系统(包括临床决策支持)、临床研究系统、转化生物信息学、全球公共卫生信息学和许多其他信息学子领域的文章,这些文章帮助我们通过信息技术了解和解决人类健康和疾病。
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