Modeling of hemodialysis patient hemoglobin: a data mining exploration

• Main Author: Bries, Michael Francis
• Other Authors: Kusiak, Andrew
• Format: Others
• Language: English
• Published: University of Iowa 2007
• Subjects: Data mining; clustering; anemia; hemodialysis; decision trees; Industrial Engineering
• Online Access: https://ir.uiowa.edu/etd/180; https://ir.uiowa.edu/cgi/viewcontent.cgi?article=1365&context=etd

Data mining is emerging as an important tool in many areas of research and industry. Companies and organizations are increasingly interested in applying data mining tools in order to increase the value added by their data collections systems. Nowhere is this potential more important than in the healthcare industry. As medical records systems become more standardized and commonplace, data quantity increases with much of it going unanalyzed. Data mining can begin to leverage some of this data into tools that help clinicians organize data and make decisions. These modeling techniques are explored in the following text. Through the use of clustering and classification techniques, accurate models of a dialysis patient's current status are derived.