Analysis of otolaryngology patient information forms using text mining techniques
2009
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Advisor: Yrd. Doç. Dr. Uğur Bilge
Abstract (EN)
Approximately 90% of the world?s data is held in unstructured or semi-structured formats. Since 1960s, several methods have been developed to transform the data into a structured and machine processable format. Text mining has seen an increasing interest in the 2000s, for discovering unknown information and facts from the free-text data.Recently, the number of text mining applications in medical sciences has grown with an increasing rate. Unstructured free-text data, such as patient discharge notes and reports, doctor?s notes, clinical trials and studies, research reports, web pages and hospital records are some of the important data sources for physicians. To analyze and access this kind of data by human efforts is difficult and time consuming. Considering the time it takes for decision making, and accesing accurate and required information about patients, this kind of systems have become necessary.In this study, we developed a software system to transform 600 discharge notes, from the Department of Otolaryngology of Akdeniz University, to a structured form, enabling physicians to access patient information, extracting clinical data from the discharge notes, and codifying them for analysis. First of all, discharge notes which are kept as Microsoft Office Word documents have been transformed into a data table after preprocessing. Data in the data table can be stored in XML format or as Microsoft Office Excel spreadsheets. A query form has also been designed for enabling physicians to access the patient data. To identify the significant content words within each section keyword lists have been used and content words have been converted into a predefined coded structure. Association Rules, that is one of the methods of the traditional data mining, has been applied to the coded data in order to discover the relations between entities/concepts. In the future, we plan to develop a more comprehensive and professional software that could be used in the other departments of the hospital.
Author
Başak Oğuz
Institution
How to Cite
Başak Oğuz (Master Thesis). Analysis of otolaryngology patient information forms using text mining techniques, 2009, Akdeniz University.
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