高强度、多模态与非相干性：助力健康技术数据分析中的重大挑战。
High intensity, multimodality and incoherence: grand challenges in the analysis of data for health-enabling technologies.
作者信息
Kohlmann Martin, Gietzelt Matthias, Marschollek Michael, Song Bianying, Wolf Klaus-Hendrik, Haux Reinhold
机构信息
Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig -Institute of Technology and Hannover Medical School, Germany.
出版信息
Stud Health Technol Inform. 2013;192:967.
When working with health-enabling technologies, researchers all over the world usually have to analyze highly intensive, multimodal and incoherent data. We explain that there is a lack of systematization within the set of methods of analysis suitable for these data. As a first step towards a methodology in this context, we present the Systematic Nomenclature for Contexts, Analysis Methods and Problems in Health-Enabling Technologies (SNOCAP-HET).
在使用促进健康技术时，世界各地的研究人员通常必须分析高度密集、多模态且不连贯的数据。我们解释说，适用于这些数据的分析方法集缺乏系统性。作为朝着这方面方法迈出的第一步，我们提出了促进健康技术中的上下文、分析方法和问题系统命名法（SNOCAP-HET）。
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