Data mining in BMI

Content:

The two classes of BMI 502 in this week cover the two topics in biomedical informatics study from quantitative perspectives, one is the data mining technology used in computer science, and the other is the technology used in statistics study.

The lecturer from computer science gave us a general introduction to the data mining, and introduce the usually used the data mining methods, which include the classification, cluster, association rule discovery, and regression. From this class, I understand that the data mining is a really inter-disciplinary subject, which covers the statistics and computer science. Actually, the principle of data mining is rooted in the statistics, and implemetation and developing of data mining rely on the computer science study. The principle and basic algorithm of the above methods were introduced briefly, and the practical applications of these method in our daily life are also discussed in some simple examples. Further more, the more detailed discussion in this class focused on the method on classification. The decision tree classification, rule based classification, nearest-neighbor classifiers, and artificial neural network methods are mentioned in this class. Although no further detialed discussion was around each methods in classification, the introduction also initiates my interesting to dig in deeper to learn about how these methods were realized and the particular advantages and disadvantages of these methods.

Because the classification has broad applications in artificial intelligence, and especially can be used in decision making procedure, so I think the classification is close related to the clinical decision making support study in biomedical informatics field. For example, for the traditional clinical diagnostic procedure conducted by clinical professionals, the decision making procedure is actually very like a decision tree classifing procedure. The clinical professional classify the disease of a patient from the symptoms of the patient, and think about if... then the patient probably is ..., and further from another symptom, the if... then... procedure is performed again, until the last conclusion can be obtained. This procedure is actually the same procedure of the decision tree classification. Therefore, the computer can also be trained to learn the rules to build the classification tree model, and based on this model to provide decisioin support in clinical practice.

There is one book about data mining is very classic for get a further idea about data mining method.
Introduction to data mining. Author: Pang-Ning Tan, Michael Steinbach, Vipin Kumar.

Hope it can be useful


Posted by  Di Pan