NILE: Fast Natural Language Processing for Electronic Health Records
This provides a practical tool for medical informatics and data science, though it is incremental as it builds on existing methods for speed and accuracy improvements.
The paper tackles the challenge of processing narrative text in Electronic Health Records by introducing NILE, a fast NLP package that achieves processing speeds hundreds to thousands of times faster than existing software while matching the accuracy of top models on benchmark data.
Objective: Narrative text in Electronic health records (EHR) contain rich information for medical and data science studies. This paper introduces the design and performance of Narrative Information Linear Extraction (NILE), a natural language processing (NLP) package for EHR analysis that we share with the medical informatics community. Methods: NILE uses a modified prefix-tree search algorithm for named entity recognition, which can detect prefix and suffix sharing. The semantic analyses are implemented as rule-based finite state machines. Analyses include negation, location, modification, family history, and ignoring. Result: The processing speed of NILE is hundreds to thousands times faster than existing NLP software for medical text. The accuracy of presence analysis of NILE is on par with the best performing models on the 2010 i2b2/VA NLP challenge data. Conclusion: The speed, accuracy, and being able to operate via API make NILE a valuable addition to the NLP software for medical informatics and data science.