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Towards Personalization of Diabetes Therapy Using Computerized Decision Support and Machine Learning: Some Open Problems and Challenges

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8700))

Abstract

Diabetes mellitus (DM) is a growing global disease which highly affects the individual patient and represents a global health burden with financial impact on national health care systems. Type 1 DM can only be treated with insulin, whereas for patients with type 2 DM a wide range of therapeutic options are available. These options include lifestyle changes such as change of diet and an increase of physical activity, but also administration of oral or injectable antidiabetic drugs. The diabetes therapy, especially with insulin, is complex. Therapy decisions include various medical and life-style related information. Computerized decision support systems (CDSS) aim to improve the treatment process in patient’s self-management but also in institutional care. Therefore, the personalization of the patient’s diabetes treatment is possible at different levels. It can provide medication support and therapy control, which aid to correctly estimate the personal medication requirements and improves the adherence to therapy goals. It also supports long-term disease management, aiming to develop a personalization of care according to the patient’s risk stratification. Personalization of therapy is also facilitated by using new therapy aids like food and activity recognition systems, lifestyle support tools and pattern recognition for insulin therapy optimization. In this work we cover relevant parameters to personalize diabetes therapy, how CDSS can support the therapy process and the role of machine learning in this context. Moreover, we identify open problems and challenges for the personalization of diabetes therapy with focus on decision support systems and machine learning technology.

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Authors and Affiliations

  1. HEALTH - Institute for Biomedicine and Health Sciences, JOANNEUM RESEARCH Forschungsgesellschaft mbH, Graz, Austria

    Klaus Donsa, Stephan Spat, Peter Beck & Thomas R. Pieber

  2. Division of Endocrinology and Metabolism, Department of Internal Medicine, Medical University of Graz, Graz, Austria

    Thomas R. Pieber

  3. Institute for Medical Informatics, Statistics and Documentation Research Unit HCI-KDD, Medical University Graz, Auenbruggerplatz 2/V, 8036, Graz, Austria

    Andreas Holzinger

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  1. Klaus Donsa
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  2. Stephan Spat
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  3. Peter Beck
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  4. Thomas R. Pieber
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  5. Andreas Holzinger
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Correspondence to Klaus Donsa .

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  1. Research Unit HCI-KDD, Medical University of Graz, Graz, Austria

    Andreas Holzinger

  2. Industrial HCI Research Lab, Fraunhofer Institute, Lemgo, Germany

    Carsten Röcker

  3. Human-Computer Interaction Center, RWTH Aachen University, Aachen, Germany

    Martina Ziefle

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Donsa, K., Spat, S., Beck, P., Pieber, T.R., Holzinger, A. (2015). Towards Personalization of Diabetes Therapy Using Computerized Decision Support and Machine Learning: Some Open Problems and Challenges. In: Holzinger, A., Röcker, C., Ziefle, M. (eds) Smart Health. Lecture Notes in Computer Science(), vol 8700. Springer, Cham. https://doi.org/10.1007/978-3-319-16226-3_10

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