Automatic recognition of disorders, findings, pharmaceuticals and body structures from clinical text
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TLDR
The entity recognition results for the individual entities Disorder and Finding show that it is meaningful to separate the general category Medical Problem into these two more granular entity types, e.g. for knowledge mining of co-morbidity relations and disorder-finding relations.About:
This article is published in Journal of Biomedical Informatics.The article was published on 2014-06-01 and is currently open access. It has received 112 citations till now. The article focuses on the topics: Named-entity recognition & Conditional random field.read more
Citations
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Journal ArticleDOI
Clinical Natural Language Processing in languages other than English: opportunities and challenges
Aurélie Névéol,Hercules Dalianis,Sumithra Velupillai,Sumithra Velupillai,Guergana Savova,Pierre Zweigenbaum +5 more
TL;DR: This paper offers the first broad overview of clinical Natural Language Processing (NLP) for languages other than English and identifies major challenges and opportunities that will affect the impact of NLP on clinical practice and public health studies in a context that encompasses English as well as other languages.
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Application of text mining in the biomedical domain
TL;DR: The most important techniques that are used for a text mining are introduced and an overview of the text mining tools that are currently being used and the type of problems they are typically applied for are given.
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Incorporating Dictionaries into Deep Neural Networks for the Chinese Clinical Named Entity Recognition
TL;DR: In this article, a new model which combines data-driven deep learning approaches and knowledge-driven dictionary approaches was proposed to handle the clinical named entity recognition task, and two different architectures that extend the bi-directional long short-term memory neural network and five different feature representation schemes were also proposed.
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Identifying adverse drug event information in clinical notes with distributional semantic representations of context
TL;DR: This study reports on the creation of an annotated corpus of Swedish health records for the purpose of learning to identify information pertaining to ADEs present in clinical notes, and leverages models of distributional semantics are shown to improve the predictive performance.
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Named Entity Recognition Over Electronic Health Records Through a Combined Dictionary-based Approach
Alexandra Pomares Quimbaya,Alejandro Sierra Múnera,Rafael Andrés González Rivera,Julián Camilo Daza Rodríguez,Oscar Mauricio Muñoz Velandia,Ángel Alberto García Peña,Cyril Labbé +6 more
TL;DR: This paper proposes a combined approach for the recognition of named entities in narrative texts, a composition of three different methods that shows an improvement of the recall and a limited impact on precision for the named entity recognition process.
References
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Proceedings Article
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
TL;DR: This work presents iterative parameter estimation algorithms for conditional random fields and compares the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data.
Book
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Dan Jurafsky,James Martin +1 more
TL;DR: This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora, to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation.
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Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications
Guergana Savova,James J. Masanz,Philip V. Ogren,Jiaping Zheng,Sunghwan Sohn,Karin C Kipper-Schuler,Christopher G. Chute +6 more
TL;DR: The cTAKES annotations are the foundation for methods and modules for higher-level semantic processing of clinical free-text, and its components, specifically trained for the clinical domain, create rich linguistic and semantic annotations.
Journal ArticleDOI
Inter-coder agreement for computational linguistics
TL;DR: It is argued that weighted, alpha-like coefficients, traditionally less used than kappa-like measures in computational linguistics, may be more appropriate for many corpus annotation tasks—but that their use makes the interpretation of the value of the coefficient even harder.