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Clustering Large-scale Diverse Electronic Medical Records to Aid Annotation for Generic Named Entity Recognition

Nithin Haridas, Yubin Kim
2020 Web Search and Data Mining  
We demonstrate the effect of lack of coverage in training data for a supervised generic named entity recognition(GNER) task and the impact of clustering on the task.  ...  We propose that clustering clinical text documents is an effective way to aid the annotation effort and ensure coverage.  ...  Generic named entity recognition is used to generate structured representation of a clinical text document by identifying biomedical concepts in the text.  ... 
dblp:conf/wsdm/HaridasK20 fatcat:623afwiohjcn5hrv456bf37qfe

Learning for Biomedical Information Extraction: Methodological Review of Recent Advances [article]

Feifan Liu, Jinying Chen, Abhyuday Jagannatha, Hong Yu
2016 arXiv   pre-print
In addition, we dive into open information extraction and deep learning, two emerging and influential techniques and envision next generation of BioIE.  ...  Biomedical information extraction (BioIE) is important to many applications, including clinical decision support, integrative biology, and pharmacovigilance, and therefore it has been an active research  ...  [55] proposed a novel kernel named "sliding tree kernel", which is an improved tree kernel specific to named entity recognition(NER) task.  ... 
arXiv:1606.07993v1 fatcat:7d5om7zxxzhoviiriasrfwg3xi

An analytical study of information extraction from unstructured and multidimensional big data

Kiran Adnan, Rehan Akbar
2019 Journal of Big Data  
Generally, the process of SLR is divided into three main phases named as planning, conduct and reporting the review.  ...  Among these statistics, it was also predicted that unstructured data from diverse sources will grow up to 90% in few years.  ...  Named entity recognition (NER) Named Entity Recognition is one of the important tasks of IE systems used to extract descriptive entities.  ... 
doi:10.1186/s40537-019-0254-8 fatcat:qy5l55um7feeblec4hxohr3pqa

Biomedical Natural Language ProcessingKevin Bretonnel Cohen and Dina Demner-Fushman (University of Colorado School of Medicine, and National Library of Medicine)John Benjamins Publishing (Book series on Natural Language Processing, edited by Ruslan Mitkov, volume 11), 2014, 160 pp; hardbound, ISBN 978-90-272-4997-5

Jin-Dong Kim
2017 Computational Linguistics  
Thanks also to the reviewers for their helpful comments.  ...  Acknowledgments We would like to thank all members of our research group, IT for Health, for their support and input.  ...  Firstly, to aid the recognition and annotation of single and multiword named entities and secondly, in an indirect way, to aid the appropriate recognition of (unknown) multiword expressions/tokens.  ... 
doi:10.1162/coli_r_00281 fatcat:6abwqppd3bgyvkzfc2mq6kzo6u

Text Mining the History of Medicine

Paul Thompson, Riza Theresa Batista-Navarro, Georgios Kontonatsios, Jacob Carter, Elizabeth Toon, John McNaught, Carsten Timmermann, Michael Worboys, Sophia Ananiadou, Luis M. Rocha
2016 PLoS ONE  
Historical text archives constitute a rich and diverse source of information, which is becoming increasingly readily accessible, due to large-scale digitisation efforts.  ...  We applied the pipeline to two large-scale medical document archives covering wide temporal ranges as the basis for the development of a publicly accessible semantically-oriented search system.  ...  Acknowledgments We would like to thank Peter Ashman, Publishing Director of the BMJ, for granting permission to use the contents of the BMJ archive and Dr.  ... 
doi:10.1371/journal.pone.0144717 pmid:26734936 pmcid:PMC4703377 fatcat:imdgobcsdreyrfvdftxfo375la

Natural Language Processing for EHR-Based Computational Phenotyping

Zexian Zeng, Yu Deng, Xiaoyu Li, Tristan Naumann, Yuan Luo
2018 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
This article reviews recent advances in applying natural language processing (NLP) to Electronic Health Records (EHRs) for computational phenotyping.  ...  However, the construction of keyword and rule lists requires significant manual effort, which is difficult to scale.  ...  Acknowledgment This work was supported in part by NIH Grant 1R21LM012618-01, NLM Biomedical Informatics Training Grant 2T15 LM007092-22, and the Intel Science and Technology Center for Big Data.  ... 
doi:10.1109/tcbb.2018.2849968 pmid:29994486 pmcid:PMC6388621 fatcat:wsksxvr7lfbgjowrsymghld64u

Data-driven materials research enabled by natural language processing and information extraction

Elsa A. Olivetti, Jacqueline M. Cole, Edward Kim, Olga Kononova, Gerbrand Ceder, Thomas Yong-Jin Han, Anna M. Hiszpanski
2020 Applied Physics Reviews  
DATA AVAILABILITY Data sharing is not applicable to this article as no new data were created or analyzed in this study.  ...  referred to as named entity recognition (NER), (5) entity relation extraction, and (6) named entity linking.  ...  There is a need for training data to develop entity-recognition models.  ... 
doi:10.1063/5.0021106 fatcat:75aap3lkjvhprleptl3bbp6w64

Mining the pharmacogenomics literature--a survey of the state of the art

U. Hahn, K. B. Cohen, Y. Garten, N. H. Shah
2012 Briefings in Bioinformatics  
of relevant named entities (e.g. genes, gene variants and proteins, diseases and other pathological phenomena, drugs and other chemicals relevant for medical treatment), as well as various forms of relations  ...  knowledge at different levels of formality and specificity, software architectures for building complex and scalable text analytics pipelines and Web services grounded to them, as well as comprehensiveways  ...  Medication information is one of the most important types of clinical data in electronic medical records (EMRs).  ... 
doi:10.1093/bib/bbs018 pmid:22833496 pmcid:PMC3404399 fatcat:por4dnthkrcxjdsir6uc64kdaq

Natural Language Processing for EHR-Based Computational Phenotyping [article]

Zexian Zeng, Yu Deng, Xiaoyu Li, Tristan Naumann, Yuan Luo
2018 arXiv   pre-print
This article reviews recent advances in applying natural language processing (NLP) to Electronic Health Records (EHRs) for computational phenotyping.  ...  However, the construction of keyword and rule lists requires significant manual effort, which is difficult to scale.  ...  Nationwide adoption of Electronic Health Records (EHRs) has given rise to a large amount of digital health data, which can be used for secondary analysis [2] .  ... 
arXiv:1806.04820v2 fatcat:fo5ck7rpgzhb7dgmqfjc3bdw7y

Applications of Natural Language Processing in Biodiversity Science

Anne E. Thessen, Hong Cui, Dmitry Mozzherin
2012 Advances in Bioinformatics  
Large-scale mining of information from the literature is necessary if biology is to transform into a data-driven science. A computer can handle the volume but cannot make sense of the language.  ...  Progress has been made in developing algorithms for automated annotation of taxonomic text, identification of taxonomic names in text, and extraction of morphological character information from taxonomic  ...  Nathan Wilson for thoughtful comments on an early version of this manuscript and productive discussion.  ... 
doi:10.1155/2012/391574 pmid:22685456 pmcid:PMC3364545 fatcat:qsqdapr7bvdkbaro2e4kkg3v2q

Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis

S. Velupillai, D. Mowery, B. R. South, M. Kvist, H. Dalianis
2015 IMIA Yearbook of Medical Informatics  
Three key clinical NLP subtasks that enable such analysis were identified: 1) developing more efficient methods for corpus creation (annotation and de-identification), 2) generating building blocks for  ...  A plethora of new clinical use cases are emerging due to established health care initiatives and additional patient-generated sources through the extensive use of social media and other devices.  ...  Acknowledgements We wish to thank Pierre Zweigenbaum  ... 
doi:10.15265/iy-2015-009 pmid:26293867 pmcid:PMC4587060 fatcat:4cqwat2q2jhkphvrfytfowmyge

Text mining for traditional Chinese medical knowledge discovery: A survey

Xuezhong Zhou, Yonghong Peng, Baoyan Liu
2010 Journal of Biomedical Informatics  
In order to contribute to this still growing field, this paper presents (1) a comparative introduction to TCM and modern biomedicine, (2) a survey of the related information sources of TCM, (3) a review  ...  It has been shown that the TCM knowledge obtained from clinical practice has become a significant complementary source of information for modern biomedical sciences.  ...  It has been recognized that the electronic medical record (EMR) for both inpatients and outpatients is a significant data source for TCM research [79] .  ... 
doi:10.1016/j.jbi.2010.01.002 pmid:20074663 fatcat:bdjjxcqa2nh4rasqrdoqddfwqe

Clinical Text Data in Machine Learning: Systematic Review

Irena Spasic, Goran Nenadic
2020 JMIR Medical Informatics  
Supervised learning was successfully used where clinical codes integrated with free-text notes into electronic health records were utilized as class labels.  ...  Active learning was explored to iteratively sample a subset of data for manual annotation as a strategy for minimizing the annotation effort while maximizing the predictive performance of the model.  ...  Acknowledgments The authors gratefully acknowledge the support from the Engineering and Physical Sciences Research Council for HealTex-UK Healthcare Text Analytics Research Network (Grant number EP/N027280  ... 
doi:10.2196/17984 pmid:32229465 fatcat:zbnsn4hi4zakhpukefktb72yo4

Automatic Labeled Dialogue Generation for Nursing Record Systems

Tittaya Mairittha, Nattaya Mairittha, Sozo Inoue
2020 Journal of Personalized Medicine  
Secondly, we introduce an idea for intent and entity labeling by using feature embeddings and semantic similarity-based clustering.  ...  We also empirically evaluate different embedding methods for learning good representations that are most suitable to use with our data and clustering tasks.  ...  In our prior work on a dialogue-based annotation for activity recognition [61] , we suggested that for the usage across all users, there were very general action words and name-specific activities (e.g  ... 
doi:10.3390/jpm10030062 pmid:32708593 fatcat:hgg5l6j5zzawbfhikjork7y77q

An Effective Approach to Biomedical Information Extraction with Limited Training Data [article]

Siddhartha Jonnalagadda
2011 arXiv   pre-print
The proposed work on concept extraction amalgamates for the first time two diverse research areas -distributional semantics and information extraction.  ...  Overall, the two main contributions of this work include the application of sentence simplification to association extraction as described above, and the use of distributional semantics for concept extraction  ...  ., 2010 ) that statistically created word clusters (Brown et al., 1992; Clark, 2000) could be used to improve named entity recognition.  ... 
arXiv:1107.5752v2 fatcat:n7rtfgpvarh7do2xar6opjec5q
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