Multiple hierarchical classification of free-text clinical guidelines.

Objective: Manual classification of free-text documents within a predefined hierarchy, commonly required in the medical domain, is highly time consuming task. We present an approach based on supervised learning to automate the classification of clinical guidelines into predefined hierarchical conceptual categories.

Methods And Material: Given a set of hierarchically categorized documents in the training stage the learning algorithm exploits the hierarchical structure of the concepts in order to overcome the low number of training examples.