Multi-topic Aspects in Clinical Text Classification

This paper investigates multi-topic aspects in automatic classification of clinical free text. In many practical situations, we need to deal with documents overlapping with multiple topics. Automatic assignment of multiple ICD-9CM codes to clinical free text in medical records is a typical multi-topic text classification problem. In this paper, we facilitate two different views on multi-topics. The Closed Topic Assumption (CTA) regards an absence of topics for a document as an explicit declaration that this document does not belong to those absent topics. In contrast, the Open Topic Assumption (OTA) considers the missing topics as neutral topics. This paper compares performances of various interpretations of a multi-topic Text Classification problem into a Machine Learning problem. Experimental results show that the characteristics of multi-topic assignments in the Medical NLP Challenge data is OTA-oriented.

• Type: Conference
• Year: 2007
• Where: BIBM
• Authors: Yutaka Sasaki, Brian Rea, Sophia Ananiadou
• Keywords: BIBM 2007, Bioinformatics, Clinical Free Text, Multi-topic Text Classification, Text Classification Problem