  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.
