Programming techniques for improving rule readability for rule-based information extraction natural language processing pipelines of unstructured and semi-structured medical texts

Abstract

Background Extraction of medical terms and their corresponding values from semi-structured and unstructured texts of medical reports can be a time-consuming and error-prone process. Methods of natural language processing (NLP) can help define an extraction pipeline for accomplishing a structured format transformation strategy. Objectives In this paper, we build an NLP pipeline to extract values of the classification of malignant tumors (TNM) from unstructured and semi-structured pathology reports and import them further to a structured data source for a clinical study. Our research interest is not focused on standard performance metrics like precision, recall, and F-measure on the test and validation data. We discuss how with the help of software programming techniques the readability of rule-based (RB) information extraction (IE) pipelines can be improved, and therefore minimize the time to correct or update the rules, and efficiently import them to another programming language. Methods The extract rules were manually programmed with training data of TNM classification and tested in two separate pipelines based on design specifications from domain experts and data curators. Firstly we implemented each rule directly in one line for each extraction item. Secondly, we reprogrammed them in a readable fashion through decomposition and intention-revealing names for the variable declaration. To measure the impact of both methods we measure the time for the fine-tuning and programming of the extractions through test data of semi-structured and unstructured texts. Results We analyze the benefits of improving through readability of the writing of rules, through parallel programming with regular expressions (REGEX), and the Apache Uima Ruta language (AURL). The time for correcting the readable rules in AURL and REGEX was significantly reduced. Complicated rules in REGEX are decomposed and intention-revealing declarations were reprogrammed in AURL in 5 min. Conclusion We discuss the importance of factor readability and how can it be improved when programming RB text IE pipelines. Independent of the features of the programming language and the tools applied, a readable coding strategy can be proven beneficial for future maintenance and offer an interpretable solution for understanding the extraction and for transferring the rules to other domains and NLP pipelines.