Data Mining Process in Hospital Information System
Data mining aims at discovering novel, interesting and useful knowledge from databases. Conventionally, the data is analyzed manually. Many hidden and potentially useful relationships may not be recognized by the analyst. Many organizations including modern hospitals are capable of generating and collecting a huge amount of data.
Data stored in medical databases are growing in an increasingly rapid way. Analyzing that data is crucial for medical decision making and management. It has been widely recognized that medical data analysis can lead to an enhancement of health care by improving the performance of patient management tasks. There are two main aspects that define the need for medical data analysis.
- Support of specific knowledge-based problem solving activities through the analysis of patients’ raw data collected in monitoring.
- Discovery of new knowledge that can be extracted through the analysis ofrepresentative collections of example cases, described by symbolic or numericdescriptors.
For these purposes, the increase in database size makes traditional manual data analysis to be insufficient. New research fields such as knowledge discovery in databases (KDD) have rapidly grown in recent years. The main step in the knowledge discovery process, called data mining, deals with the problem of finding interesting regularities and patterns in data.
A simple data mining process model mainly includes 6 steps:
- Assembling the data.
- The data warehouse.
- Relational database and flat files.
- Mining the data.
- Interpreting the results.
- Result application.