A methodological framework for the analysis of highly intensive, multimodal and heterogeneous data in the context of health-enabling technologies and ambient-assisted living. (September 2014)

• Record Type: Journal Article
• Authors: Kohlmann, Martin; Gietzelt, Matthias; Haux, Reinhold; Song, Bianying; Wolf, Klaus-Hendrik; Marschollek, Michael
• Is Part Of: Informatics for health & social care. Volume 39:Number 3/4(2014)
• Page Start: 294
• Page End: 304
• Publication Date: 2014-09
• Subjects: Medicine -- Information services -- Periodicals; Medical informatics -- Periodicals; Medicine -- Data processing -- Periodicals; 025.0661
• DOI: 10.3109/17538157.2014.931847
• Languages: English
• ISSNs: 1753-8157

Abstract

<abstract> <title> <x xml:space="preserve">Abstract</x> </title> <p> <italic>Background</italic>: When applying health-enabling technologies (HET), researchers are faced with analyzing highly intensive, multimodal and heterogeneous data sets. Experience has shown that there is a lack of understanding concerning the relationship of analysis methods suitable for such data sets and their appropriate application.</p> <p> <italic>Objectives</italic>: The objective of this paper is to describe the present situation when analyzing data of HET and the main problems in this context, to present a nomenclature suitable for analysis methods in the context of HET, to present an example dealing with geriatric diseases that highlights the problems and the urgent need for results and to explain some steps for future work.</p> <p> <italic>Methods</italic>: Nomenclatures as standard tools in information processing are applied.</p> <p> <italic>Results</italic>: We present an open three-axial mono-hierarchical nomenclature called SNOCAP-HET. Moreover, we explain other ideas to overcome the lack of systematization within the set of analysis methods suitable for HET.</p> <p> <italic>Conclusions</italic>: Our approach allows for an extension of SNOCAP-HET and will allow for the development and evaluation of different measures for the appropriateness of analysis methods given a certain highly intensive, multimodal and heterogeneous data set in the context of HET. Our final future aim is to obtain better results when analyzing medical data.</p> </abstract>