Supervised Classification of Healthcare Text Data Based on Context-Defined Categories

Abstract

Achieving a good success rate in supervised classification analysis of a text dataset, where the relationship between the text and its label can be extracted from the context, but not from isolated words in the text, is still an important challenge facing the fields of statistics and machine learning. For this purpose, we present a novel mathematical framework. We then conduct a comparative study between established classification methods for the case where the relationship between the text and the corresponding label is clearly depicted by specific words in the text. In particular, we use logistic LASSO, artificial neural networks, support vector machines, and decision-tree-like procedures. This methodology is applied to a real case study involving mapping Consolidated Framework for Implementation and Research (CFIR) constructs to health-related text data and achieves a prediction success rate of over 80% when just the first 55% of the text, or more, is used for training and the remaining for testing. The results indicate that the methodology can be useful to accelerate the CFIR coding process.