NILE: Fast Natural Language Processing for Electronic Health Records

TL;DR

NILE is a fast and accurate NLP tool for EHR analysis that significantly outperforms existing software in speed while maintaining high accuracy, facilitating medical informatics research.

Contribution

This paper introduces NILE, a novel NLP package for EHR analysis that combines a modified prefix-tree search with rule-based semantic analysis, achieving unprecedented speed and competitive accuracy.

Findings

NILE is hundreds to thousands times faster than existing NLP tools.

NILE's accuracy matches top models on the 2010 i2b2/VA NLP challenge.

NILE operates via API, enhancing usability in medical informatics.

Abstract

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.