Applications of Data Mining in the Healthcare Industry

Applications of Data Mining in the Healthcare Industry

John Wang, Xiaohua Hu, Dan Zhu
Copyright: © 2008 |Pages: 6
DOI: 10.4018/978-1-59904-889-5.ch010
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The workflow of health care organizations involves the generation and collection of various kinds of data relating to clinical practices, clinical trials, patient information, resource administration, policies, and research. Traditionally, statistical techniques are used to derive some operational information from the data. Data mining, a new method, provides the opportunity to derive, in an exploratory and interactive manner, valuable health care knowledge in terms of associations, sequential patterns, classifications, predictions, and symbolic rules. Such inductively-derived health care knowledge can provide strategic insights into the practice delivery of health care. As the medical field expands, it is the duty of each physician to evaluate and protect each patient from diseases, side effects, and medical mishaps. Armed with a scalpel, stethoscope, and other accruements, physicians are now armed with data mining as a tool for expanding their knowledge base. Data mining is available to every aspect within the health care industry. It is multifaceted and used in areas like insurance to detect fraud, the pharmaceutical industry to evaluate side effects of drugs, and even detection of certain diseases based on genetics.

Key Terms in this Chapter

Neural Networks: Also referred to as artificial intelligence (AI), which utilizes predictive algorithms.

Clinical Pathways: Integrated care pathways are multidisciplinary care plans where diagnosis and treatment are performed by medical teams.

Machine Learning: Concerned with the development of algorithms and techniques, which allow computers to “learn.”

Pattern Recognition: The act of taking in raw data and taking an action based on the category of the data. It is a field within the area of machine learning.

Data Mining Surveillance System (DMSS): Uses data mining techniques to discover unsuspected, useful patterns of infections and antimicrobial resistance from the analysis of hospital laboratory data.

Data Mining: Also known as knowledge discovery in databases (KDD), data mining is the process of automatically searching large volumes of data for patterns. Data mining is a fairly recent and contemporary topic in computing.

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