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Implementation of an interactive pattern mining framework on electronic health record datasets

  • Large collections of electronic patient records contain a broad range of clinical information highly relevant for data analysis. However, they are maintained primarily for patient administration, and automated methods are required to extract valuable knowledge for predictive, preventive, personalized and participatory medicine. Sequential pattern mining is a fundamental task in data mining which can be used to find statistically relevant, non-trivial temporal dependencies of events such as disease comorbidities. This works objective is to use this mining technique to identify disease associations based on ICD-9-CM codes data of the entire Taiwanese population obtained from Taiwan’s National Health Insurance Research Database. This thesis reports the development and implementation of the Disease Pattern Miner – a pattern mining framework in a medical domain. The framework was designed as a Web application which can be used to run several state-of-the-art sequence mining algorithms on electronic health records, collect and filter the results to reduce the number of patterns to a meaningful size, and visualize the disease associations as an interactive model in a specific population group. This may be crucial to discover new disease associations and offer novel insights to explain disease pathogenesis. A structured evaluation of the data and models are required before medical data-scientist may use this application as a tool for further research to get a better understanding of disease comorbidities.

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Author:Vitaliy Ostapchuk
Referee:Daniel Pfeifer, Shabbir Syed Abdul
Document Type:Master's Thesis
Year of Completion:2019
Publishing Institution:Hochschule Heilbronn
Granting Institution:Hochschule Heilbronn
Date of final exam:2019/05/06
Release Date:2019/05/09
Tag:Data Mining; elektronische Gesundheitsakten
GND Keyword:Datenanalyse; Krankenunterlagen
Pagenumber:VIII, 65 Seiten
DDC classes:600 Technik, Medizin, angewandte Wissenschaften
Access Right:Frei zugänglich
Licence (German):License LogoCreative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International