数据分析与数据挖掘:生物医学信息学中的当前问题
Data analysis and data mining: current issues in biomedical informatics.
作者信息
Bellazzi R, Diomidous M, Sarkar I N, Takabayashi K, Ziegler A, McCray A T
机构信息
University of Pavia, Dipartimento di Informatica e Sistemistica, Via Ferrata 1, 27100 Pavia (PV), Italy.
出版信息
Methods Inf Med. 2011;50(6):536-44. doi: 10.3414/ME11-06-0002.
BACKGROUND
Medicine and biomedical sciences have become data-intensive fields, which, at the same time, enable the application of data-driven approaches and require sophisticated data analysis and data mining methods. Biomedical informatics provides a proper interdisciplinary context to integrate data and knowledge when processing available information, with the aim of giving effective decision-making support in clinics and translational research.
OBJECTIVES
To reflect on different perspectives related to the role of data analysis and data mining in biomedical informatics.
METHODS
On the occasion of the 50th year of Methods of Information in Medicine a symposium was organized, which reflected on opportunities, challenges and priorities of organizing, representing and analysing data, information and knowledge in biomedicine and health care. The contributions of experts with a variety of backgrounds in the area of biomedical data analysis have been collected as one outcome of this symposium, in order to provide a broad, though coherent, overview of some of the most interesting aspects of the field.
RESULTS
The paper presents sections on data accumulation and data-driven approaches in medical informatics, data and knowledge integration, statistical issues for the evaluation of data mining models, translational bioinformatics and bioinformatics aspects of genetic epidemiology.
CONCLUSIONS
Biomedical informatics represents a natural framework to properly and effectively apply data analysis and data mining methods in a decision-making context. In the future, it will be necessary to preserve the inclusive nature of the field and to foster an increasing sharing of data and methods between researchers.
背景
医学和生物医学科学已成为数据密集型领域,这同时使得数据驱动方法得以应用,并且需要复杂的数据分析和数据挖掘方法。生物医学信息学提供了一个恰当的跨学科环境,以便在处理现有信息时整合数据和知识,目的是在临床和转化研究中提供有效的决策支持。
目的
反思与数据分析和数据挖掘在生物医学信息学中的作用相关的不同观点。
方法
在《医学信息方法》创刊50周年之际,组织了一次研讨会,探讨了生物医学和医疗保健领域中数据、信息和知识的组织、表示及分析的机遇、挑战和优先事项。作为本次研讨会的成果之一,收集了生物医学数据分析领域不同背景专家的文稿,以便对该领域一些最有趣的方面提供一个广泛而连贯的概述。
结果
本文介绍了医学信息学中的数据积累和数据驱动方法、数据与知识整合、数据挖掘模型评估的统计学问题、转化生物信息学以及遗传流行病学的生物信息学方面等章节。
结论
生物医学信息学是在决策背景下正确且有效地应用数据分析和数据挖掘方法的自然框架。未来,有必要保持该领域的包容性,并促进研究人员之间数据和方法的共享不断增加。