Learning Regular Expressions for Interpretable Medical Text Classification Using a Pool-based Simulated Annealing and Word-vector Models
This provides an interpretable alternative to black-box deep learning methods for medical text classification, though it is incremental as it builds on existing rule-based approaches.
The paper tackles the problem of generating interpretable regular expressions for medical text classification by automating their creation with a constructive heuristic and optimizing them using Pool-based Simulated Annealing, reducing manual effort while maintaining high-quality solutions.
In this paper, we propose a rule-based engine composed of high quality and interpretable regular expressions for medical text classification. The regular expressions are auto generated by a constructive heuristic method and optimized using a Pool-based Simulated Annealing (PSA) approach. Although existing Deep Neural Network (DNN) methods present high quality performance in most Natural Language Processing (NLP) applications, the solutions are regarded as uninterpretable black boxes to humans. Therefore, rule-based methods are often introduced when interpretable solutions are needed, especially in the medical field. However, the construction of regular expressions can be extremely labor-intensive for large data sets. This research aims to reduce the manual efforts while maintaining high-quality solutions