Indexing ICD-9 codes for free-textual clinical diagnosis records by a new ensemble classifier

摘要

An expert system which can support in indexing automatically an ICD-9 code for a clinical diagnosis record is necessary and in great demand for hospitals. The ICD-9 code determines reimbursement amount and an incorrect code may result in fining. In this paper we present a new expert system to index automatically an ICD-9 code with respect to two perspectives. First, the system analyses the free-textual medical documents as would be necessary to activate the uses of natural language process and text mining. A free-textual document has to be represented by a large number of vocabulary words as analogy with a high dimensional data vector. Second, we drive a new ensemble classifier which combines the uses of the majority voting approach with multiple learning algorithms and the boosting approach at the same time. The motivation is stimulated in that when a predicted ICD-9 code of a majority voting of a clinical diagnosis record is incorrect, the record needs to be trained more often. The experimental results show that the proposed ensemble technique is able to achieve simultaneously stability and performance in terms of classification accuracy.