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In medical fields, text classification is one of the most important tasksthat can significantly reduce human workload through structured informationdigitization and intelligent decision support. Despite the popularity oflearning-based text classification techniques, it is hard for human tounderstand or manually fine-tune the classification results for betterprecision and recall, due to the black box nature of learning. This studyproposes a novel regular expression-based text classification method making useof genetic programming (GP) approaches to evolve regular expressions that canclassify a given medical text inquiry with satisfactory precision and recallwhile allow human to read the classifier and fine-tune accordingly ifnecessary. Given a seed population of regular expressions (can be randomlyinitialized or manually constructed by experts), our method evolves apopulation of regular expressions according to chosen fitness function, using anovel regular expression syntax and a series of carefully chosen reproductionoperators. Our method is evaluated with real-life medical text inquiries froman online healthcare provider and shows promising performance. Moreimportantly, our method generates classifiers that can be fully understood,checked and updated by medical doctors, which are fundamentally crucial formedical related practices.

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