scholarly journals SZTE-NLP: Clinical Text Analysis with Named Entity Recognition

2014 ◽  
Author(s):  
Melinda Katona ◽  
Richárd Farkas
Keyword(s):  
Text Analysis ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Clinical Text ◽  
Named Entity

scholarly journals MetaMap Lite: an evaluation of a new Java implementation of MetaMap

2017 ◽  
Vol 24 (4) ◽  
pp. 841-844 ◽  
Author(s):  
Dina Demner-Fushman ◽  
Willie J Rogers ◽  
Alan R Aronson
Keyword(s):  
Text Analysis ◽  
Text Processing ◽  
Named Entity Recognition ◽  
Biomedical Literature ◽  
Entity Recognition ◽  
Biomedical Text ◽  
Clinical Text ◽  
Unified Medical Language System ◽  
Named Entity ◽  
Medical Language

Abstract MetaMap is a widely used named entity recognition tool that identifies concepts from the Unified Medical Language System Metathesaurus in text. This study presents MetaMap Lite, an implementation of some of the basic MetaMap functions in Java. On several collections of biomedical literature and clinical text, MetaMap Lite demonstrated real-time speed and precision, recall, and F1 scores comparable to or exceeding those of MetaMap and other popular biomedical text processing tools, clinical Text Analysis and Knowledge Extraction System (cTAKES) and DNorm.

Developing a RadLex-based Named Entity Recognition Tool for Mining Textual Radiology Reports (Preprint)

2020 ◽  
Author(s):  
Shintaro Tsuji ◽  
Andrew Wen ◽  
Naoki Takahashi ◽  
Hongjian Zhang ◽  
Katsuhiko Ogasawara ◽  
...  
Keyword(s):  
Named Entity Recognition ◽  
Noun Phrases ◽  
General Purpose ◽  
Entity Recognition ◽  
Free Text ◽  
Clinical Text ◽  
Named Entity ◽  
Radiology Reports ◽  
Two Measures ◽  
F Measure

BACKGROUND Named entity recognition (NER) plays an important role in extracting the features of descriptions for mining free-text radiology reports. However, the performance of existing NER tools is limited because the number of entities depends on its dictionary lookup. Especially, the recognition of compound terms is very complicated because there are a variety of patterns. OBJECTIVE The objective of the study is to develop and evaluate a NER tool concerned with compound terms using the RadLex for mining free-text radiology reports. METHODS We leveraged the clinical Text Analysis and Knowledge Extraction System (cTAKES) to develop customized pipelines using both RadLex and SentiWordNet (a general-purpose dictionary, GPD). We manually annotated 400 of radiology reports for compound terms (Cts) in noun phrases and used them as the gold standard for the performance evaluation (precision, recall, and F-measure). Additionally, we also created a compound-term-enhanced dictionary (CtED) by analyzing false negatives (FNs) and false positives (FPs), and applied it for another 100 radiology reports for validation. We also evaluated the stem terms of compound terms, through defining two measures: an occurrence ratio (OR) and a matching ratio (MR). RESULTS The F-measure of the cTAKES+RadLex+GPD was 32.2% (Precision 92.1%, Recall 19.6%) and that of combined the CtED was 67.1% (Precision 98.1%, Recall 51.0%). The OR indicated that stem terms of “effusion”, "node", "tube", and "disease" were used frequently, but it still lacks capturing Cts. The MR showed that 71.9% of stem terms matched with that of ontologies and RadLex improved about 22% of the MR from the cTAKES default dictionary. The OR and MR revealed that the characteristics of stem terms would have the potential to help generate synonymous phrases using ontologies. CONCLUSIONS We developed a RadLex-based customized pipeline for parsing radiology reports and demonstrated that CtED and stem term analysis has the potential to improve dictionary-based NER performance toward expanding vocabularies.

scholarly journals A comprehensive study of named entity recognition in Chinese clinical text

2014 ◽  
Vol 21 (5) ◽  
pp. 808-814 ◽  
Author(s):  
J. Lei ◽  
B. Tang ◽  
X. Lu ◽  
K. Gao ◽  
M. Jiang ◽  
...  
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
Clinical Text ◽  
Named Entity ◽  
Comprehensive Study

scholarly journals A study of active learning methods for named entity recognition in clinical text

2015 ◽  
Vol 58 ◽  
pp. 11-18 ◽  
Author(s):  
Yukun Chen ◽  
Thomas A. Lasko ◽  
Qiaozhu Mei ◽  
Joshua C. Denny ◽  
Hua Xu
Keyword(s):  
Active Learning ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Learning Methods ◽  
Clinical Text ◽  
Named Entity

scholarly journals A Survey on Recent Named Entity Recognition and Relationship Extraction Techniques on Clinical Texts

2021 ◽  
Vol 11 (18) ◽  
pp. 8319
Author(s):  
Priyankar Bose ◽  
Sriram Srinivasan ◽  
William C. Sleeman ◽  
Jatinder Palta ◽  
Rishabh Kapoor ◽  
...  
Keyword(s):  
Information Extraction ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Future Research ◽  
Clinical Text ◽  
Named Entity ◽  
Relationship Extraction ◽  
New Information ◽  
Comprehensive Survey ◽  
Future Research Directions

Significant growth in Electronic Health Records (EHR) over the last decade has provided an abundance of clinical text that is mostly unstructured and untapped. This huge amount of clinical text data has motivated the development of new information extraction and text mining techniques. Named Entity Recognition (NER) and Relationship Extraction (RE) are key components of information extraction tasks in the clinical domain. In this paper, we highlight the present status of clinical NER and RE techniques in detail by discussing the existing proposed NLP models for the two tasks and their performances and discuss the current challenges. Our comprehensive survey on clinical NER and RE encompass current challenges, state-of-the-art practices, and future directions in information extraction from clinical text. This is the first attempt to discuss both of these interrelated topics together in the clinical context. We identified many research articles published based on different approaches and looked at applications of these tasks. We also discuss the evaluation metrics that are used in the literature to measure the effectiveness of the two these NLP methods and future research directions.

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