Data Mining and Analytics for Exploring Bulgarian Diabetic Register

Autor:	Svetla Boytcheva, Dimitar Tcharaktchiev, Zhivko Angelov, Galia Angelova
Rok vydání:	2018
Předmět:	business.industry Computer science Big data Specific risk medicine.disease computer.software_genre Health informatics Text mining Knowledge extraction Business analytics Analytics medicine Prediabetes Data mining business computer
Zdroj:	Communications in Computer and Information Science ISBN: 9783319965529 DAMDID/RCDL (Selected Papers)
DOI:	10.1007/978-3-319-96553-6_2
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::e8738bc1939346bc2f93cb26a98cb72d https://doi.org/10.1007/978-3-319-96553-6_2 Zobrazit plný text záznamu