Abstract
This paper discusses the need of building diabetic registers in order to monitor the disease development and assess the prevention and treatment plans. The automatic generation of a nation-wide Diabetes Register in Bulgaria is presented, using outpatient records submitted to the National Health Insurance Fund in 2010–2014 and updated with data from outpatient records for 2015–2016. The construction relies on advanced automatic analysis of free clinical texts and business analytics technologies for storing, maintaining, searching, querying and analyzing data. Original frequent pattern mining algorithms enable to discover maximal frequent itemsets of simultaneous diseases for diabetic patients. We show how comorbidities, identified for patients in the prediabetes period, can help to define alerts about specific risk factors for Diabetes Mellitus type 2, and thus might contribute to prevention. We also claim that the synergy of modern analytics and data mining tools transforms a static archive of clinical patient records to a sophisticated knowledge discovery and prediction environment.
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Notes
- 1.
International Classification of Diseases and Related Health Problems 10th Revision. http://apps.who.int/classifications/icd10/browse/2015/en.
- 2.
WHO, BMI Classification http://apps.who.int/bmi/index.jsp?introPage=intro_3.html.
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Acknowledgements
This research is partially supported by grant IZIDA 02/4 (SpecialIZed Data MIning MethoDs Based on Semantic Attributes), funded by the Bulgarian National Science Fund in 2017–2019. The authors acknowledge also the support of Medical University – Sofia, the National Health Insurance Fund and the Bulgarian Ministry of Health.
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Boytcheva, S., Angelova, G., Angelov, Z., Tcharaktchiev, D. (2018). Data Mining and Analytics for Exploring Bulgarian Diabetic Register. In: Kalinichenko, L., Manolopoulos, Y., Malkov, O., Skvortsov, N., Stupnikov, S., Sukhomlin, V. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2017. Communications in Computer and Information Science, vol 822. Springer, Cham. https://doi.org/10.1007/978-3-319-96553-6_2
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