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Comparison of a semi-automatic annotation tool and a natural language processing application for the generation of clinical statement entries

C.-H. Lin, N.-Y. Wu, W.-S. Lai, D.-M. Liou
2014 JAMIA Journal of the American Medical Informatics Association  
Using HL7 clinical document architecture (CDA) as the example, we developed three pipelines to generate entry-level CDA documents.  ...  This study aimed to use a novel approach for the generation of entry-level interoperable clinical documents.  ...  We developed the CAS converter for cTAKES to transform the CAS file into an entry-level CDA document.  ... 
doi:10.1136/amiajnl-2014-002991 pmid:25332357 pmcid:PMC4433379 fatcat:crr6sxpgtzfzvl2frrtg6yweie

Revisiting Medical Entity Recognition through the Guidelines of the Aurora Initiative

Praveen Kumar, Sabah Mohammed, Arnold Kim, Jinan Fiaidhi
2016 International Journal of Bio-Science and Bio-Technology  
as can conversion the recognized entities into CDA (Clinical Document Architecture) format for interoperability.  ...  Named entity recognition (NER) is a subtask of Clinical documentation processing which is important not only for text analysis but knowledge extraction.  ...  ., developed a model to generate standardised CDA R2 document from EMR (Excel file) whereas Lin [23] proposed a pipeline to generate CDA entries from free -text.  ... 
doi:10.14257/ijbsbt.2016.8.4.13 fatcat:amthxl4m4zd5pdtahwl2qu76ry

The Yale cTAKES extensions for document classification: architecture and application

Vijay Garla, Vincent Lo Re, Zachariah Dorey-Stein, Farah Kidwai, Matthew Scotch, Julie Womack, Amy Justice, Cynthia Brandt
2011 JAMIA Journal of the American Medical Informatics Association  
Clinical natural-languageprocessing systems annotate the syntax and semantics of clinical text; however, feature extraction and representation for document classification pose technical challenges.  ...  Methods The authors developed extensions to the clinical Text Analysis and Knowledge Extraction System (cTAKES) that simplify feature extraction, experimentation with various feature representations, and  ...  . 1 2 The electronic medical record stores much of the relevant information in the form of unstructured free text.  ... 
doi:10.1136/amiajnl-2011-000093 pmid:21622934 pmcid:PMC3168305 fatcat:oangcqniyfhinl75kp3toa5xpy

Launching into clinical space with medspaCy: a new clinical text processing toolkit in Python [article]

Hannah Eyre
2021 arXiv   pre-print
Our toolkit includes several core components and facilitates rapid development of pipelines for clinical text.  ...  By utilizing spaCy's clear and easy-to-use conventions, medspaCy enables development of custom pipelines that integrate easily with other spaCy-based modules.  ...  In contrast, spaCy * provides a robust architecture for building and sharing custom, high-performance NLP pipelines by taking an object-oriented view of text.  ... 
arXiv:2106.07799v1 fatcat:nunfifgvprfzbjhfq5uaoo6pia

v3NLP Framework: Tools to Build Applications for Extracting Concepts from Clinical Text

Guy Divita, Marjorie Carter, Le-Thuy Tran, Doug Redd, Qing T. Zeng, Scott Duvall, Matthew H. Samore, Adi V. Gundlapalli
2016 eGEMs  
concepts from clinical text.  ...  Substantial amounts of clinically significant information are contained only within the narrative of the clinical notes in electronic medical records. v3NLP Framework is a set of best of breed functionalities  ...  We acknowledge the staff, resources and facilities of the VA Salt Lake City IDEAS Center 2.0 for providing a rich and stimulating environment for NLP research.  ... 
doi:10.13063/2327-9214.1228 pmid:27683667 pmcid:PMC5019303 fatcat:hbsndtmntvgxnd3mhvkl3y2vga

Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: The SHARPn project

Susan Rea, Jyotishman Pathak, Guergana Savova, Thomas A. Oniki, Les Westberg, Calvin E. Beebe, Cui Tao, Craig G. Parker, Peter J. Haug, Stanley M. Huff, Christopher G. Chute
2012 Journal of Biomedical Informatics  
The platform was deployed to (1) receive source EHR data in several formats, (2) generate structured data from EHR narrative text, and (3) normalize the EHR data using common detailed clinical models and  ...  The architecture of this prototype SHARPn platform is presented.  ...  The authors would like to thank the following individuals that assisted in this project or provided the computing infrastructure that made it possible: Mayo Clinic Jay  ... 
doi:10.1016/j.jbi.2012.01.009 pmid:22326800 pmcid:PMC4905766 fatcat:aqpu7badxngcbgbvf2fu4u54ha

Unlocking the Potential of Electronic Health Records for Health Research

Seungwon Lee, Yuan Xu, Adam G D'Souza, Elliot A Martin, Chelsea Doktorchik, Zilong Zhang, Hude Quan
2020 International Journal of Population Data Science  
EHR data elements can be of many types, which can be categorized as structured, unstructured free-text, and imaging data.  ...  The Sunrise Clinical Manager (SCM) EHR is one example of an inpatient EHR system, which covers the city of Calgary (Alberta, Canada).  ...  Abdel Aziz Shaheen for assistance with preparation of this manuscript.  ... 
doi:10.23889/ijpds.v5i1.1123 pmid:32935049 pmcid:PMC7473254 fatcat:3cpwcq5wrvfermwz3kk2yblf5a

Interactive Cohort Identification of Sleep Disorder Patients Using Natural Language Processing and i2b2

W. Chen, R. Kowatch, S. Lin, M. Splaingard, Y. Huang
2015 Applied Clinical Informatics  
Conclusion: Natural language processing was shown to be powerful for quickly converting large amount of semi-structured or unstructured clinical data into discrete concepts, which in combination of intuitive  ...  Results: 26,550 concepts were extracted with 99% being textual concepts. 1.01 million facts were extracted from sleep study documents such as demographic information, sleep study lab results, medications  ...  Acknowledgement The authors would like to acknowledge Megan Reynolds, our domain expert, for providing the gold standard, Richard Hoyt for the discussion on i2b2, and Florine Shivers for the editing.  ... 
doi:10.4338/aci-2014-11-ra-0106 pmid:26171080 pmcid:PMC4493335 fatcat:fcyrs2kcvfg5rje6wdwmhyi3q4

Subword Clusters as Light-Weight Interlingua for Multilingual Document Retrieval

Udo Hahn, Kornél G. Markó, Stefan Schulz
2005 Machine Translation Summit  
We introduce a light-weight interlingua for a crosslanguage document retrieval system in the medical domain.  ...  Documents, as well as queries, are mapped to this interlingua level on which retrieval operations are performed.  ...  Morphosemantic Processing Figure 1 depicts how source documents are converted into an interlingual representation by a threestep procedure. We start with orthographic normalization.  ... 
dblp:conf/mtsummit/HahnMS05 fatcat:5twwzenypbd4zhvnwoenksyxoi

Crossing Languages in Text Retrieval via an Interlingua

Udo Hahn, Kornél G. Markó, Michael Poprat, Stefan Schulz, Joachim Wermter, Percy Nohama
2004 Open research Areas in Information Retrieval  
We report on the empirical evaluation of both approaches on a large medical document collection (the Ohsumed corpus).  ...  We introduce an interlingua-based approach to cross-language information retrieval, in which queries, as well as documents, are mapped onto a language-independent concept layer on which retrieval operations  ...  Conclusions We presented an interlingua approach to cross-language retrieval on a medical document col lection.  ... 
dblp:conf/riao/HahnMPSWN04 fatcat:tw7dhqbnbnef5pu5ynsrpxb2re

Document Automation Architectures and Technologies: A Survey [article]

Mohammad Ahmadi Achachlouei, Omkar Patil, Tarun Joshi, Vijayan N. Nair
2021 arXiv   pre-print
The objective of DA is to reduce the manual effort during the generation of documents by automatically integrating input from different sources and assembling documents conforming to defined templates.  ...  This paper surveys the current state of the art in document automation (DA).  ...  XML structures can be mapped into HTML, plain text, or other XML structures using an Extensible Style Sheet Transformation (XSLT), which can be used to convert an XML document into other formats recursively  ... 
arXiv:2109.11603v1 fatcat:alqvdban5ndr7m3gjjtqqumk7y

Patient Cohort Retrieval using Transformer Language Models [article]

Sarvesh Soni, Kirk Roberts
2020 arXiv   pre-print
Given the recent advancements in the field of document retrieval, we map the task of CR to a document retrieval task and apply various deep neural models implemented for the general domain tasks.  ...  We apply deep learning-based language models to the task of patient cohort retrieval (CR) with the aim to assess their efficacy.  ...  It is an open-source NLP system for extracting information from the clinical text that is based on UIMA (Unstructured Information Management Architecture) framework and used often for clinical IE 34  ... 
arXiv:2009.05121v1 fatcat:aggnzkq32fgpjgpnyfy6ywmje4

Patient Cohort Retrieval using Transformer Language Models

Sarvesh Soni, Kirk Roberts
2021 AMIA Annual Symposium Proceedings  
Given the recent advancements in the field of document retrieval, we map the task of CR to a document retrieval task and apply various deep neural models implemented for the general domain tasks.  ...  We apply deep learning-based language models to the task of patient cohort retrieval (CR) with the aim to assess their efficacy.  ...  It is an open-source NLP system for extracting information from the clinical text that is based on UIMA (Unstructured Information Management Architecture) framework and used often for clinical IE 34  ... 
pmid:33936491 pmcid:PMC8075458 fatcat:ir2cz5gxjzckzoi7ufeqd5amua

Clinical natural language processing for radiation oncology: A review and practical primer

Danielle S. Bitterman, Timothy A. Miller, Raymond H. Mak, Guergana K. Savova
2021 International Journal of Radiation Oncology, Biology, Physics  
NLP algorithms convert unstructured free text data into structured data that can be extracted and analyzed at scale.  ...  In medicine, this unlocking of the rich, expressive data within clinical free text in the electronic medical records (EMR) will help untap the full potential of big data for research and clinical purposes  ...  Information extraction enables clinical data to be automatically extracted from free text for downstream analysis. Here, an NLP pipeline processes unstructured clinical text to structured data.  ... 
doi:10.1016/j.ijrobp.2021.01.044 pmid:33545300 fatcat:vhlkvw7kbzfw5p6dg3jigbkrse

caTIES: a grid based system for coding and retrieval of surgical pathology reports and tissue specimens in support of translational research

Rebecca S Crowley, Melissa Castine, Kevin Mitchell, Girish Chavan, Tara McSherry, Michael Feldman
2010 JAMIA Journal of the American Medical Informatics Association  
The system fills an important need for textderived clinical data in translational research such as tissue-banking and clinical trials.  ...  for concept-based text mining; and (3) regulatory and security model for supporting multicenter collaborative research.  ...  Acknowledgements We thank Lucy Cafeo at the University of Pittsburgh Department of Biomedical Informatics for expert preparation and review of the manuscript.  ... 
doi:10.1136/jamia.2009.002295 pmid:20442142 pmcid:PMC2995710 fatcat:cotiub7yfbahfarpfn3urykbaq
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