Data-Driven Regular Expressions Evolution for Medical Text Classification Using Genetic Programming

In medical fields, text classification is one of the most important tasks that can significantly reduce human work-load through structured information digitization and intelligent decision support. Despite the popularity of learning-based text classification techniques, it is hard for human to understand or manually fine-tune the classification for better precision and recall, due to the black box nature of learning. This study proposes a novel regular expression-based text classification method making use of genetic programming (GP) approaches to evolve regular expressions that can classify a given medical text inquiry with satisfaction. Given a seed population of regular expressions (randomly initialized or manually constructed by experts), our method evolves a population of regular expressions, using a novel regular expression syntax and a series of carefully chosen reproduction operators. Our method is evaluated with real-life medical text inquiries from an online healthcare provider and shows promising performance. More importantly, our method generates classifiers that can be fully understood, checked and updated by medical doctors, which are fundamentally crucial for medical related practices.