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

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• 关键词：Personalization ; Machine learning ; Decision support ; Diabetes mellitus
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：8700
• 期：1
• 页码：237-260
• 全文大小：259 KB
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  13. Cleveringa, F.G.W., Gorter, K.J., van den Donk, M., van Gijsel, J., Rutten, G.E.H.M.: Computerized decision support systems in primary care for type 2 diabetes patients only improve patients-outcomes when combined with feedback on performance and case management: a systematic review. Diabetes Technol. Ther. 15, 180-92 (2013)
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  15. Heise, T., Hermanski, L., Nosek, L., Feldman, A., Rasmussen, S., Haahr, H.: Insulin degludec: four times lower pharmacodynamic variability than insulin glargine under steady-state conditions in type 1 diabetes. Diabetes Obes. Metab. 14, 859-64 (2012)
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• 作者单位：Klaus Donsa (16)
  Stephan Spat (16)
  Peter Beck (16)
  Thomas R. Pieber (16) (17)
  Andreas Holzinger (18)
  
  16. HEALTH - Institute for Biomedicine and Health Sciences, JOANNEUM RESEARCH Forschungsgesellschaft mbH, Graz, Austria
  17. Division of Endocrinology and Metabolism, Department of Internal Medicine, Medical University of Graz, Graz, Austria
  18. Institute for Medical Informatics, Statistics and Documentation Research Unit HCI-KDD, Medical University Graz, Auenbruggerplatz 2/V, 8036, Graz, Austria
  
• 丛书名：Smart Health
• ISBN：978-3-319-16226-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

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.