Knowledge Discovery and Medical Informatics
IMAN ABDALLA AMR;
Abstract
Nowadays, the healthcare sector has been facing a mounting competitive situation amongst service providers accompanied with growing cost pressure and rising quality demands. At the same time, vast quantities of healthcare data need to be exploited and mined for data-driven decision support services and quality management. The core issues of medical quality management processes can be measured by indicators. The search/mining for indicators in the data enabling the quality manager to check validity of existing indicators and to discover new indicators as well as to refine and adjust given indicators.
This study represents an approach of applying knowledge discovery in a heuristic way for medical quality management. We proposed a heuristic knowledge discovery process integrating interest-driven and data-driven analyses to focus the search area and to filter the results. The focused task of this study is to investigate selected indicators targeted on the main issues of medical quality management and to discover new indicators in real-world patient data Therefore, we applied this process to structured patient data sets from several clinics and hospitals in a developing country. This work assists to identify patterns in patient data that help to improve the efficiency and quality of clinical processes in terms of medical, administrative quality, and cost-benefit ratio.
As a background, this study introduces a brief idea about Knowledge Discovery in Databases (K.DD) besides a survey of some leading knowledge discovery software packages, indicating mainly the data mining tasks and techniques they provide. Moreover, a survey of KDD applications in medical domain according to our proposed feature classification scheme developed a vision about the various medical fields to apply KDD, the Data Mining techniques deployed, and the approaches to accomplish KDD aiming at maximizing its efficiency.
As the first step of knowledge discovery process is to understand the application domain by learning relevant prior knowledge, we discuss the nature of medical data and the common difficulties in its handling. To insure wherever
This study represents an approach of applying knowledge discovery in a heuristic way for medical quality management. We proposed a heuristic knowledge discovery process integrating interest-driven and data-driven analyses to focus the search area and to filter the results. The focused task of this study is to investigate selected indicators targeted on the main issues of medical quality management and to discover new indicators in real-world patient data Therefore, we applied this process to structured patient data sets from several clinics and hospitals in a developing country. This work assists to identify patterns in patient data that help to improve the efficiency and quality of clinical processes in terms of medical, administrative quality, and cost-benefit ratio.
As a background, this study introduces a brief idea about Knowledge Discovery in Databases (K.DD) besides a survey of some leading knowledge discovery software packages, indicating mainly the data mining tasks and techniques they provide. Moreover, a survey of KDD applications in medical domain according to our proposed feature classification scheme developed a vision about the various medical fields to apply KDD, the Data Mining techniques deployed, and the approaches to accomplish KDD aiming at maximizing its efficiency.
As the first step of knowledge discovery process is to understand the application domain by learning relevant prior knowledge, we discuss the nature of medical data and the common difficulties in its handling. To insure wherever
Other data
| Title | Knowledge Discovery and Medical Informatics | Other Titles | استكشاف المعرفة والمعلوماتية الطبية | Authors | IMAN ABDALLA AMR | Issue Date | 2005 |
Attached Files
| File | Size | Format | |
|---|---|---|---|
| IMAN ABDALLA AMR.pdf | 1.49 MB | Adobe PDF | View/Open |
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