As health care delivery is firmly dependent on accurate and detailed clinical data, an important task of medical informatics is to facilitate access to and enhance the quality of this information, thereby improving the accuracy of clinical outcomes. The first step of such computer analysis is to extract data from various medical reports and reformat them into a structured coded form. Often, this information conversion task can be followed by text classification. Text classifiers can be built to automatically detect and extract the medical condition features in the medical reports and convert them into predefined medical codes or terms.
Although such a classifier is likely to be built through manual work, considering the difficulties and expenses, as well as coordination between medical experts and knowledge engineers, researchers therefore have been investigated the use of inductive learning algorithms, in the pursuit of automatically generating classifiers for clinical reports.
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