NILE: Fast Natural Language Processing for Electronic Health Records

Sheng Yu; Tianrun Cai; Tianxi Cai

NILE: Fast Natural Language Processing for Electronic Health Records

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1311.6063 v5 pith:JEQLXGCF submitted 2013-11-23 cs.CL

Sheng Yu , Tianrun Cai , Tianxi Cai This is my paper · ORCID

classification cs.CL

keywords nilemedicaldataprocessingaccuracyanalysesanalysiselectronic

verification ladder T0 review T1 audit T2 compute T3 formal

0 comments

read the original 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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

GENIE: Generative Note Information Extraction model for structuring EHR data
cs.CL 2025-01 conditional novelty 5.0 of 10

A single fine-tuned 8B-parameter LLM extracts medical terms plus six clinical attributes from EHR notes in one pass, outperforming cTAKES and MetaMap on a small human-annotated test set.

Pith tools