ArticleJournal of biomedical informatics2018
Trie-based rule processing for clinical NLP: A use-case study of n-trie, making the ConText algorithm more efficient and scalable.
Article in Journal of biomedical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- SPELL: A scalable NLP method using regular expressions and large language models for clinical information extraction.Computer methods and programs in biomedicine · 2026Article
- Leveraging Data Science for Conducting Observational Studies: Highlighting Advantages and Limitations Throughout the Evaluation of a Use Case.Mayo Clinic proceedings. Digital health · 2026Article
- Large language model derived regular expressions for sleep phenotyping from electronic health record: a feasibility study.Sleep advances : a journal of the Sleep Research Society · 2026Article
- Scalable information extraction from free text electronic health records using large language models.BMC medical research methodology · 2025Article
- Multi-task transfer learning for the prediction of entity modifiers in clinical text: application to opioid use disorder case detection.Journal of biomedical semantics · 2024Article
- A deep learning approach for medication disposition and corresponding attributes extraction.Journal of biomedical informatics · 2023Article
- Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods.BMC bioinformatics · 2023Article
- Identifying Patients Who Meet Criteria for Genetic Testing of Hereditary Cancers Based on Structured and Unstructured Family Health History Data in the Electronic Health Record: Natural Language Processing Approach.JMIR medical informatics · 2022Article
- Extraction of Treatment Information From Electronic Health Records and Evaluation of Testosterone Recovery in Patients With Prostate Cancer.JCO clinical cancer informatics · 2022Article
- A Hybrid Model for Family History Information Identification and Relation Extraction: Development and Evaluation of an End-to-End Information Extraction System.JMIR medical informatics · 2021Article
- Portable Automated Surveillance of Surgical Site Infections Using Natural Language Processing: Development and Validation.Annals of surgery · 2020Article
- A customizable deep learning model for nosocomial risk prediction from critical care notes with indirect supervision.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Facilitating information extraction without annotated data using unsupervised and positive-unlabeled learning.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Using Natural Language Processing to improve EHR Structured Data-based Surgical Site Infection Surveillance.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- Deep Learning from Incomplete Data: Detecting Imminent Risk of Hospital-acquired Pneumonia in ICU Patients.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- Determination of Marital Status of Patients from Structured and Unstructured Electronic Healthcare Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
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2 authors.
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Abstract
objectiveTo develop and evaluate an efficient Trie structure for large-scale, rule-based clinical natural language processing (NLP), which we call n-trie.
backgroundDespite the popularity of machine learning techniques in natural language processing, rule-based systems boast important advantages: distinctive transparency, ease of incorporating external knowledge, and less demanding annotation requirements. However, processing efficiency remains a major obstacle for adopting standard rule-base NLP solutions in big data analyses.
methodsWe developed n-trie to specifically address the token-based nature of context detection, an important facet of clinical NLP that is known to slow down NLP pipelines. N-trie, a new rule processing engine using a revised Trie structure, allows fast execution of lexicon-based NLP rules. To determine its applicability and evaluate its performance, we applied the n-trie engine in an implementation (called FastContext) of the ConText algorithm and compared its processing speed and accuracy with JavaConText and GeneralConText, two widely used Java ConText implementations, as well as with a standalone machine learning NegEx implementation, NegScope.
resultsThe n-trie engine ran two orders of magnitude faster and was far less sensitive to rule set size than the comparison implementations, and it proved faster than the best machine learning negation detector. Additionally, the engine consistently gained accuracy improvement as the rule set increased (the desired outcome of adding new rules), while the other implementations did not.
conclusionsThe n-trie engine is an efficient, scalable engine to support NLP rule processing and shows the potential for application in other NLP tasks beyond context detection.
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