数据挖掘技术及其在临床恶性肿瘤诊疗中的应用

介绍数据挖掘相关技术,包括特征选择、离群值检测模型、聚类模型、关联规则模型、分类模型、集成学习算法等方面,对数据挖掘在临床恶性肿瘤诊断、预后及管理中的应用进行具体阐述。     

A Survey of Data Mining and Deep Learning in Bioinformatics

The fields of medicine science and health informatics have made great progress recently and have led to in-depth analytics that is demanded by generation, collection and accumulation of massive data. Meanwhile, we are entering a new period where novel technologies are starting to analyze and explore knowledge from tremendous amount of data, bringing limitless potential for information growth. One fact that cannot be ignored is that the techniques of machine learning and deep learning applications play a more significant role in the success of bioinformatics exploration from biological data point of view, and a linkage is emphasized and established to bridge these two data analytics techniques and bioinformatics in both industry and academia. This survey concentrates on the review of recent researches using data mining and deep learning approaches for analyzing the specific domain knowledge of bioinformatics. The authors give a brief but pithy summarization of numerous data mining algorithms used for preprocessing, classification and clustering as well as various optimized neural network architectures in deep learning methods, and their advantages and disadvantages in the practical applications are also discussed and compared in terms of their industrial usage. It is believed that in this review paper, valuable insights are provided for those who are dedicated to start using data analytics methods in bioinformatics.     

生物医学文本挖掘研究热点分析

为了解生物医学文本挖掘的研究现状和评估未来的发展方向,以美国国立图书馆 PubMed中收录的 2000年 1月-2015年3月发表的生物医学文本挖掘研究文献记录为样本来源,提取文献记录的主要主题词进行频次统计后截取高频主题词,形成高频主题词-论文矩阵,根据高频主题词在同一篇论文中的共现情况对其进行聚类分析,根据高频主题词聚类分析结果和对应的类标签文献,分析当前生物医学文本挖掘研究的热点。结果显示,当前文本挖掘在生物医学领域应用的主要研究热点为文本挖掘的基本技术研究、文本挖掘在生物信息学领域里的应用、文本挖掘在药物相关事实抽取中的应用3个方面。     

Contemporary use of real-world data for clinical trial conduct in the United States: a scoping review

ObjectiveReal-world data (RWD), defined as routinely collected healthcare data, can be a potential catalyst for addressing challenges faced in clinical trials. We performed a scoping review of database-specific RWD applications within clinical trial contexts, synthesizing prominent uses and themes.Materials and MethodsQuerying 3 biomedical literature databases, research articles using electronic health records, administrative claims databases, or clinical registries either within a clinical trial or in tandem with methodology related to clinical trials were included. Articles were required to use at least 1 US RWD source. All abstract screening, full-text screening, and data extraction was performed by 1 reviewer. Two reviewers independently verified all decisions.ResultsOf 2020 screened articles, 89 qualified: 59 articles used electronic health records, 29 used administrative claims, and 26 used registries. Our synthesis was driven by the general life cycle of a clinical trial, culminating into 3 major themes: trial process tasks (51 articles); dissemination strategies (6); and generalizability assessments (34). Despite a diverse set of diseases studied, <10% of trials using RWD for trial process tasks evaluated medications or procedures (5/51). All articles highlighted data-related challenges, such as missing values.DiscussionDatabase-specific RWD have been occasionally leveraged for various clinical trial tasks. We observed underuse of RWD within conducted medication or procedure trials, though it is subject to the confounder of implicit report of RWD use.ConclusionEnhanced incorporation of RWD should be further explored for medication or procedure trials, including better understanding of how to handle related data quality issues to facilitate RWD use.     

中医病案数据挖掘现状分析

从工具选择、数据采集、数据预处理、数据挖掘、结果翻译5个方面对中医病案数据挖掘研究现状进行概述,发现中医病案数据挖掘的信息主要包括症状、证型、方剂等,涉及的挖掘方法有关联规则、复杂网络、聚类、分类等。本研究可为中医临床数据挖掘选题及方法选择提供参考。     

中医证候研究中的分类算法方法学研究

中医证的研究一直是中医药现代化研究的关键之一,其核心是证候分类和诊断标准的研究,数据挖掘中的分类算法已经大量应用于中医证候的分类研究。本文评述了数据挖掘中分类算法在中医证候研究中的应用,对其中主要算法的特点、适用条件和范围进行综合分析,认为应该根据不同的研究目的,选择适当的分类算法。粗糙集和聚类分析不需要先验知识,适合进行探索性的研究;模糊集理论、神经网络和决策树需要先验知识,适合应用于分类目标比较明确的证候诊断标准研究;模糊集理论更适合与其他分类算法结合应用,产生模糊聚类、模糊神经网络、模糊粗糙集和模糊决策树等更适合中医证候分类研究的算法。在具体的辨证分类研究中,我们需要根据所研究的疾病和证型分类特点选择合适的分类算法及其组合,同时建议应该在集成多学科理论与技术的基础上进行创新,建立符合中医证候特点的分类算法。     

Novel Approach to Fuzzy-Wavelet ECG Signal Analysis for a Mobile Device

This paper describes a signal processing technique for ECG signal analysis based upon the combination of wavelet analysis and fuzzy c-means clustering. The signal analysis technique is implemented into a biomedical signal diagnostic unit that is the carry on device for the Wireless Nano-Bios Diagnostic System (WNBDS) developed at National Taiwan University. The WNBDS integrates mobile devices and remote data base servers to conduct online monitoring and remote healthcare applications. The signal analysis and diagnostic algorithms in this paper are implemented in an embedded mobile device to conduct mobile biomedical signal diagnostics. At this stage, the Electrocardiogram (ECG or EKG) is analyzed for patient health monitoring. The ECG signal processing is based on the wavelet analysis, and the diagnosis is based on fuzzy clustering. The embedded system is realized with the Windows CE operating system.     

基于本体论的电子健康档案知识库构建初探

电子健康档案具有明显的文献特征,有较强的研究价值和挖掘价值。在概述电子健康档案文献特点和生物医药语义知识库研究现状的基础上,论述了电子健康档案知识库构建的步骤、技术难点及解决思路。讨论了电子健康档案如何引入本体和本体技术以及进行语义抽取,在此基础上提出基于本体的数据挖掘技术应用于健康档案的构想,实现健康档案中医学知识的多维度关联与智能检索功能。     

The BioIntelligence Framework: a new computational platform for biomedical knowledge computing

Breakthroughs in molecular profiling technologies are enabling a new data-intensive approach to biomedical research, with the potential to revolutionize how we study, manage, and treat complex diseases. The next great challenge for clinical applications of these innovations will be to create scalable computational solutions for intelligently linking complex biomedical patient data to clinically actionable knowledge. Traditional database management systems (DBMS) are not well suited to representing complex syntactic and semantic relationships in unstructured biomedical information, introducing barriers to realizing such solutions. We propose a scalable computational framework for addressing this need, which leverages a hypergraph-based data model and query language that may be better suited for representing complex multi-lateral, multi-scalar, and multi-dimensional relationships. We also discuss how this framework can be used to create rapid learning knowledge base systems to intelligently capture and relate complex patient data to biomedical knowledge in order to automate the recovery of clinically actionable information.     

Advancing the Framework: Use of Health Data—A Report of a Working Conference of the American Medical Informatics Association

The fields of health informatics and biomedical research increasingly depend on the availability of aggregated health data. Yet, despite over fifteen years of policy work on health data issues, the United States (U.S.) lacks coherent policy to guide users striving to navigate the ethical, political, technical, and economic challenges associated with health data use. In 2007, building on more than a decade of previous work, the American Medical Informatics Association (AMIA) convened a panel of experts to stimulate discussion about and action on a national framework for health data use. This initiative is being carried out in the context of rapidly accelerating advances in the fields of health informatics and biomedical research, many of which are dependent on the availability of aggregated health data. Use of these data poses complex challenges that must be addressed by public policy. This paper highlights the results of the meeting, presents data stewardship as a key building block in the national framework, and outlines stewardship principles for the management of health information. The authors also introduce a taxonomy developed to focus definitions and terminology in the evolving field of health data applications. Finally, they identify areas for further policy analysis and recommend that public and private sector organizations elevate consideration of a national framework on the uses of health data to a top priority.     

基于医案数据研究萎缩性胃炎的辨证及组方规律

目的 通过研究慢性萎缩性胃炎的中医医案,总结整理出中医治疗慢性萎缩性胃炎的辨证以及组方规律。方法 收集治疗慢性萎缩性胃炎的相关中医医案,运用数据分析法、聚类算法以及关联规则分析等多种研究方法,整理有效数据并总结概括出中医治疗慢性萎缩性胃炎的辨证和组方用药规律。结果 本研究通过分析252份萎缩性胃炎的相关中医医案,对医案中常见的证候、具体治法、使用较高频次的中药及药物配伍间的关联规则高度概括,最终得出32个核心配伍及14个新处方。结论 中医治疗慢性萎缩性胃炎在证型、具体治法、治疗用药等多个方面有一定规律可循,因此,借助转化医学的分析方法和医案数据挖掘技术得出的萎缩性胃炎的治疗规律具有一定的可行性及临床参考价值。     

文本挖掘在生物医学领域中的应用及其系统工具

系统介绍了生物医学文本挖掘的具体流程和文本挖掘技术在生物医学领域中的应用情况,并着重从自然语言处理和本体、命名实体识别、关系抽取、文本分类与聚类、共现分析、系统工具及评价、可视化等方面分别做了阐述.     

生物医学文本知识发现的研究进展

目前,如何解决海量文本信息与知识增长缓慢的矛盾,以可信的方式发现文本中有用的模式是一项严峻的挑战。本文就国际上有关文本挖掘在生物医学领域的应用进行阐述。概念识别和发现关系研究已经取得丰硕成果,而元数据挖掘正处于起步阶段。利用元数据进行生物医学文本挖掘以及建立知识库是现阶段文本知识发现的重要任务。     

淋巴瘤医案不同聚类分析方法比较研究

目的 以淋巴瘤临床医案为范例数据,对不同聚类分析方法挖掘结果进行比较,从而分析中医医案药物聚类挖掘方法的优化方案与结果差异.方法 对淋巴瘤医案进行统一预处理与规范,运用分散性聚类中的快速聚类、结构性聚类中的层次聚类进行挖掘分析,并从算法特点、终值偏倚与临床拟合3个维度综合比较.结果 研究共涉及患者138人次,病例354...     

Collaborative Technology Use by Healthcare Teams

This paper reviews the literature on the use of collaborative technologies by healthcare teams between 1980 and 2003. Multiple databases were searched with explicit inclusion criteria that yielded 17 conceptual and empirical papers. The discussions of these literatures centered on the individual, team, and technological dimensions of collaborative technology use within healthcare teams. Results show that collaborative healthcare technologies can have positive effects on team work processes at both the individual and group level. The limited number of research studies accentuates the need for additional research in this area. Future research should focus on defining team tasks; determining which type of groupware works for a particular health setting; and exploring the effects of groupware on patient care delivery and the organization. Without research in these areas, it will be difficult to harness the full advantages of using groupware technologies by collaborative healthcare teams.     

人工智能技术在基于电子病历研究中的应用与展望

人工智能技术在临床医学领域已取得突破性进展,如诊断、影像、疾病分期分级等。电子病历蕴含疾病描述、诊断、检查、治疗等大量临床数据,在医学专家和信息学家的共同参与下,利用人工智能技术挖掘电子病历数据的研究急剧增加。虽然该方法目前存在一些局限性,但与传统人工研究相比其具有更快速、经济、方便等优势,有望更好地服务于人类健康医学事业的发展。本文对利用人工智能技术挖掘电子病历数据的现状,包括相关技术、具体实例、局限性等进行综述。     

改进型分层聚类方法对中药分类研究

传统分层聚类方法常难以处理高维数据或大样本数据.论文对算术平均、算术平均变化率等概念作了介绍,应用算术平均变化率对样本数据预处理,提出一种基于改进型的分层聚类方法对中药实验数据提取VIP并构建分类模式.实验证明,该方法是可行有效的.     

人工智能算法偏见与健康不公平的成因与对策分析

随着信息技术的发展，人工智能为疾病诊疗带来重要价值。然而，人工智能中存在算法偏见现象，可导致医疗卫生资源分配不均等问题，严重损害患者的健康公平。算法偏见是人为偏见的技术化体现，其形成与人工智能开发过程密切相关，主要源于数据收集、训练优化和输出应用3个方面。医护工作者作为患者健康的直接参与者，应采取相应措施以预防算法偏见，避免其引发健康公平问题。医护工作者需保障健康数据真实无偏见、优化人工智能的公平性和加强其输出应用的透明度，同时需思考如何处理临床实践中算法偏见引发的不公平现象，全面保障患者健康公平。本研究就健康领域中算法偏见的形成原因和应对策略展开综述，以期提高医护工作者识别和处理算法偏见的意识与能力，为保障信息化时代中的患者健康公平提供参考。     

Big biomedical data as the key resource for discovery science

Modern biomedical data collection is generating exponentially more data in a multitude of formats. This flood of complex data poses significant opportunities to discover and understand the critical interplay among such diverse domains as genomics, proteomics, metabolomics, and phenomics, including imaging, biometrics, and clinical data. The Big Data for Discovery Science Center is taking an “-ome to home” approach to discover linkages between these disparate data sources by mining existing databases of proteomic and genomic data, brain images, and clinical assessments. In support of this work, the authors developed new technological capabilities that make it easy for researchers to manage, aggregate, manipulate, integrate, and model large amounts of distributed data. Guided by biological domain expertise, the Center’s computational resources and software will reveal relationships and patterns, aiding researchers in identifying biomarkers for the most confounding conditions and diseases, such as Parkinson’s and Alzheimer’s.     

Clinical bioinformatics

Clinical bioinformatics provides biological and medical information to allow for individualized healthcare. In this review, we describe the uses of clinical bioinformatics. After the analysis of the complete human genome sequences, clinical bioinformatics enables researchers to search online biological databases and use the biological information in their medical practices. The data obtained from using microarray is extremely complicated. In clinical bioinformatics, selecting appropriate software to analyze the microarray data for medical decision making is crucial. Proteomics strategy tools usually focus on similarity searches, structure prediction, and protein modeling. In clinical bioinformatics, the proteomic data only have meaning if they are integrated with clinical data. In pharmacogenomics, clinical bioinformatics includes elaborate studies of bioinformatics tools and various facets of proteomics related to drug target identification and clinical validation. Using clinical bioinformatics, researchers apply computational and high-throughput experimental techniques to cancer research and systems biology. Meanwhile, researchers of bioinformatics and medical information have incorporated clinical bioinformatics to improve health care, using biological and medical information. Using the high volume of biological information from clinical bioinformatics will contribute to changes in practice standards in the healthcare system. We believe that clinical bioinformatics provides benefits of improving healthcare, disease prevention and health maintenance as we move toward the era of personalized medicine.