A Decision Support System for Predicting Hospitalization of Hemodialysis Patients

Hemodialysis patients might suffer from unhealthy care behaviors or long-term dialysis treatments. Ultimately they need to be hospitalized. If the hospitalization rate of a hemodialysis center is high, its quality of service would be low. Therefore, how to decrease hospitalization rate is a crucial problem for health care. In this study we combined temporal abstraction with data mining techniques for analyzing the dialysis patients' biochemical data to develop a decision support system. The mined temporal patterns are helpful for clinicians to predict hospitalization of hemodialysis patients and to suggest them some treatments immediately to avoid hospitalization.

Authors:

• Jinn-Yi Yeh
• Tai-Hsi Wu

Keywords:

• Hemodialysis
• Temporal abstract
• Data mining
• Healthcare quality.

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