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Information Extraction of Multiple Categories from Pathology Reports
2010
Australasian Language Technology Association Workshop
Pathology reports are used to store information about cells and tissues of a patient, and they are crucial to monitor the health of individuals and population groups. In this work we present an evaluation of supervised text classification models for the prediction of relevant categories in pathology reports. Our aim is to integrate automatic classifiers to improve the current workflow of medical experts, and we implement and evaluate different machine learning approaches for a large number of
dblp:conf/acl-alta/LiM10
fatcat:nchi2euhijdchp6idanwjivuc4