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In this paper, we propose a rule-based engine composed of high quality andinterpretable regular expressions for medical text classification. The regularexpressions are auto generated by a constructive heuristic method and optimizedusing a Pool-based Simulated Annealing (PSA) approach. Although existing DeepNeural Network (DNN) methods present high quality performance in most NaturalLanguage Processing (NLP) applications, the solutions are regarded asuninterpretable black boxes to humans. Therefore, rule-based methods are oftenintroduced when interpretable solutions are needed, especially in the medicalfield. However, the construction of regular expressions can be extremelylabor-intensive for large data sets. This research aims to reduce the manualefforts while maintaining high-quality solutions

Learning Regular Expressions for Interpretable Medical Text Classification Using a Pool-based Simulated Annealing and Word-vector Models