Abstract
By using an example of low antibody response to influenza vaccination, a new methodology approach is presented here, which can be introductory to research in chronic aging diseases, typically characterized with comorbidity. Starting with poor theory, this approach includes systematic health data record and approximate learning based on using data mining methods. By subsequent data mining, applied on already selected parameters, and supported by expert knowledge, selected health parameters can further be transformed, into functional pathophisiologic units. Graphical presentation of identified health disorders and their integration into a comprehensive visual model of the common biological network, provides additional information and improves our understanding of the topic, and can serve as a starting position for further research. In terms of Human-Computer Interaction, this approach seems challenging, by enabling computer automatic methods to be supported by human‘s cognitive processes, which might be a solution when managing massive biomedical data, usually weakly structured.
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Trtica-Majnarić, L. (2011). Knowledge Discovery and Expert Knowledge for Creating a Chart-Model of a Biological Network - Introductory to Research in Chronic Diseases and Comorbidity. In: Holzinger, A., Simonic, KM. (eds) Information Quality in e-Health. USAB 2011. Lecture Notes in Computer Science, vol 7058. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25364-5_24
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DOI: https://doi.org/10.1007/978-3-642-25364-5_24
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