Modern Clinical Text Mining: a Guide and Review
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Information Retrieval Application of Text Mining Also on Bio-Medical Fields
JOURNAL OF CRITICAL REVIEWS ISSN- 2394-5125 VOL 7, ISSUE 8, 2020 INFORMATION RETRIEVAL APPLICATION OF TEXT MINING ALSO ON BIO-MEDICAL FIELDS Charan Singh Tejavath1, Dr. Tryambak Hirwarkar2 1Research Scholar, Dept. of Computer Science & Engineering, Sri Satya Sai University of Technology & Medical Sciences, Sehore, Bhopal-Indore Road, Madhya Pradesh, India. 2Research Guide, Dept. of Computer Science & Engineering, Sri Satya Sai University of Technology & Medical Sciences, Sehore, Bhopal Indore Road, Madhya Pradesh, India. Received: 11.03.2020 Revised: 21.04.2020 Accepted: 27.05.2020 ABTRACT: Electronic clinical records contain data on all parts of health care. Healthcare data frameworks that gather a lot of textual and numeric data about patients, visits, medicines, doctor notes, and so forth. The electronic archives encapsulate data that could prompt an improvement in health care quality, advancement of clinical and exploration activities, decrease in clinical mistakes, and decrease in healthcare costs. In any case, the reports that contain the health record are wide-going in multifaceted nature, length, and utilization of specialized jargon, making information revelation complex. The ongoing accessibility of business text mining devices gives a one of a kind chance to remove basic data from textual information files. This paper portrays the biomedical text-mining technology. KEYWORDS: Text-mining technology, Bio-medical fields, Textual information files. © 2020 by Advance Scientific Research. This is an open-access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) DOI: http://dx.doi.org/10.31838/jcr.07.08.357 I. INTRODUCTION The corpus of biomedical data is becoming quickly. New and helpful outcomes show up each day in research distributions, from diary articles to book parts to workshop and gathering procedures. -
Notice Report
NetApp Notice Report Copyright 2020 About this document The following copyright statements and licenses apply to the software components that are distributed with the Active IQ Platform product released on 2020-05-13 05:40:47. This product does not necessarily use all the software components referred to below. Where required, source code is published at the following location. ftp://ftp.netapp.com/frm-ntap/opensource 1 Components: Component License Achilles Core 5.3.0 Apache License 2.0 An open source Java toolkit for Amazon S3 0.9.0 Apache License 2.0 Apache Avro 1.7.6-cdh5.4.2.1.1 Apache License 2.0 Apache Avro 1.7.6-cdh5.4.4 Apache License 2.0 Apache Commons BeanUtils 1.7.0 Apache License 2.0 Apache Commons BeanUtils 1.8.0 Apache License 2.0 Apache Commons CLI 1.2 Apache License 2.0 Apache Commons Codec 1.9 Apache License 2.0 Apache Commons Collections 3.2.1 Apache License 2.0 Apache Commons Compress 1.4.1 Apache License 2.0 Apache Commons Configuration 1.6 Apache License 2.0 Apache Commons Digester 1.8 Apache License 1.1 Apache Commons Lang 2.6 Apache License 2.0 Apache Commons Logging 1.2 Apache License 2.0 Apache Commons Math 3.1.1 Apache License 2.0 Apache Commons Net 3.1 Apache License 2.0 Apache Directory API ASN.1 API 1.0.0-M20 Apache License 2.0 Apache Directory LDAP API Utilities 1.0.0-M19 Apache License 2.0 Apache Directory LDAP API Utilities 1.0.0-M20 Apache License 2.0 Apache Directory Studio 2.0.0-M15 Apache License 2.0 Apache Directory Studio 2.0.0-M20 Apache License 2.0 2 Apache Hadoop Annotations 2.6.0-cdh5.4.4 Apache -
Barriers to Retrieving Patient Information from Electronic Health Record Data: Failure Analysis from the TREC Medical Records Track
Barriers to Retrieving Patient Information from Electronic Health Record Data: Failure Analysis from the TREC Medical Records Track Tracy Edinger, ND, Aaron M. Cohen, MD, MS, Steven Bedrick, PhD, Kyle Ambert, BA, William Hersh, MD Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA Abstract Objective: Secondary use of electronic health record (EHR) data relies on the ability to retrieve accurate and complete information about desired patient populations. The Text Retrieval Conference (TREC) 2011 Medical Records Track was a challenge evaluation allowing comparison of systems and algorithms to retrieve patients eligible for clinical studies from a corpus of de-identified medical records, grouped by patient visit. Participants retrieved cohorts of patients relevant to 35 different clinical topics, and visits were judged for relevance to each topic. This study identified the most common barriers to identifying specific clinic populations in the test collection. Methods: Using the runs from track participants and judged visits, we analyzed the five non-relevant visits most often retrieved and the five relevant visits most often overlooked. Categories were developed iteratively to group the reasons for incorrect retrieval for each of the 35 topics. Results: Reasons fell into nine categories for non-relevant visits and five categories for relevant visits. Non-relevant visits were most often retrieved because they contained a non-relevant reference to the topic terms. Relevant visits were most often infrequently retrieved because they used a synonym for a topic term. Conclusions : This failure analysis provides insight into areas for future improvement in EHR-based retrieval with techniques such as more widespread and complete use of standardized terminology in retrieval and data entry systems. -
Biomedical Text Mining: a Survey of Recent Progress
Chapter 14 BIOMEDICAL TEXT MINING: A SURVEY OF RECENT PROGRESS Matthew S. Simpson Lister Hill National Center for Biomedical Communications United States National Library of Medicine, National Institutes of Health [email protected] Dina Demner-Fushman Lister Hill National Center for Biomedical Communications United States National Library of Medicine, National Institutes of Health [email protected] Abstract The biomedical community makes extensive use of text mining tech- nology. In the past several years, enormous progress has been made in developing tools and methods, and the community has been witness to some exciting developments. Although the state of the community is regularly reviewed, the sheer volume of work related to biomedical text mining and the rapid pace in which progress continues to be made make this a worthwhile, if not necessary, endeavor. This chapter pro- vides a brief overview of the current state of text mining in the biomed- ical domain. Emphasis is placed on the resources and tools available to biomedical researchers and practitioners, as well as the major text mining tasks of interest to the community. These tasks include the recognition of explicit facts from biomedical literature, the discovery of previously unknown or implicit facts, document summarization, and question answering. For each topic, its basic challenges and methods are outlined and recent and influential work is reviewed. Keywords: Biomedical information extraction, named entity recognition, relations, events, summarization, question answering, literature-based discovery C.C. Aggarwal and C.X. Zhai (eds.), Mining Text Data, DOI 10.1007/978-1-4614-3223-4_14, 465 © Springer Science+Business Media, LLC 2012 466 MINING TEXT DATA 1. -
The Dzone Guide to Volume Ii
THE D ZONE GUIDE TO MODERN JAVA VOLUME II BROUGHT TO YOU IN PARTNERSHIP WITH DZONE.COM/GUIDES DZONE’S 2016 GUIDE TO MODERN JAVA Dear Reader, TABLE OF CONTENTS 3 EXECUTIVE SUMMARY Why isn’t Java dead after more than two decades? A few guesses: Java is (still) uniquely portable, readable to 4 KEY RESEARCH FINDINGS fresh eyes, constantly improving its automatic memory management, provides good full-stack support for high- 10 THE JAVA 8 API DESIGN PRINCIPLES load web services, and enjoys a diverse and enthusiastic BY PER MINBORG community, mature toolchain, and vigorous dependency 13 PROJECT JIGSAW IS COMING ecosystem. BY NICOLAI PARLOG Java is growing with us, and we’re growing with Java. Java 18 REACTIVE MICROSERVICES: DRIVING APPLICATION 8 just expanded our programming paradigm horizons (add MODERNIZATION EFFORTS Church and Curry to Kay and Gosling) and we’re still learning BY MARKUS EISELE how to mix functional and object-oriented code. Early next 21 CHECKLIST: 7 HABITS OF SUPER PRODUCTIVE JAVA DEVELOPERS year Java 9 will add a wealth of bigger-picture upgrades. 22 THE ELEMENTS OF MODERN JAVA STYLE But Java remains vibrant for many more reasons than the BY MICHAEL TOFINETTI robustness of the language and the comprehensiveness of the platform. JVM languages keep multiplying (Kotlin went 28 12 FACTORS AND BEYOND IN JAVA GA this year!), Android keeps increasing market share, and BY PIETER HUMPHREY AND MARK HECKLER demand for Java developers (measuring by both new job 31 DIVING DEEPER INTO JAVA DEVELOPMENT posting frequency and average salary) remains high. The key to the modernization of Java is not a laundry-list of JSRs, but 34 INFOGRAPHIC: JAVA'S IMPACT ON THE MODERN WORLD rather the energy of the Java developer community at large. -
1 Application of Text Mining to Biomedical Knowledge Extraction: Analyzing Clinical Narratives and Medical Literature
Amy Neustein, S. Sagar Imambi, Mário Rodrigues, António Teixeira and Liliana Ferreira 1 Application of text mining to biomedical knowledge extraction: analyzing clinical narratives and medical literature Abstract: One of the tools that can aid researchers and clinicians in coping with the surfeit of biomedical information is text mining. In this chapter, we explore how text mining is used to perform biomedical knowledge extraction. By describing its main phases, we show how text mining can be used to obtain relevant information from vast online databases of health science literature and patients’ electronic health records. In so doing, we describe the workings of the four phases of biomedical knowledge extraction using text mining (text gathering, text preprocessing, text analysis, and presentation) entailed in retrieval of the sought information with a high accuracy rate. The chapter also includes an in depth analysis of the differences between clinical text found in electronic health records and biomedical text found in online journals, books, and conference papers, as well as a presentation of various text mining tools that have been developed in both university and commercial settings. 1.1 Introduction The corpus of biomedical information is growing very rapidly. New and useful results appear every day in research publications, from journal articles to book chapters to workshop and conference proceedings. Many of these publications are available online through journal citation databases such as Medline – a subset of the PubMed interface that enables access to Medline publications – which is among the largest and most well-known online databases for indexing profes- sional literature. Such databases and their associated search engines contain important research work in the biological and medical domain, including recent findings pertaining to diseases, symptoms, and medications. -
Dsa-Cl Iii — Winter Term 2018-19
DSA-CL III — WINTER TERM 2018-19 DATA STRUCTURES AND ALGORITHMS FOR COMPUTATIONAL LINGUISTICS III CLAUS ZINN Çağrı Çöltekin https://dsacl3-2018.github.io DSA-CL III course overview What is DSA-CL III? ・Intermediate-level survey course. ・Programming and problem solving, with applications. – Algorithm: method for solving a problem. – Data structure: method to store information. ・Second part focused on Computational Linguistics Prerequisites: ・Data Structures and Algorithms for CL I ・Data Structures and Algorithms for CL II Lecturers: Tutors: Slots: ・Çağrı Çöltekin ・Marko Lozajic ・Mon 12:15 & 18:00 (R 0.02) ・Claus Zinn ・Michael Watkins ・Wed 14:15 — 18:00 (lab) Course Materials: https://dsacl3-2018.github.io 2 Coursework and grading Reading material for most lectures Weekly programming assignments Four graded assignments. 60% ・Due on Tuesdays at 11pm via electronic submission (Github Classroom) ・Collaboration/lateness policies: see web. Written exam. 40% ・Midterm practice exam 0% ・Final exam 40% 3 Honesty Statement Honesty statement: ・Feel free to cooperate on assignments that are not graded. ・Assignments that are graded must be your own work. Do not: – Copy a program (in whole or in part). – Give your solution to a classmate (in whole or in part). – Get so much help that you cannot honestly call it your own work. – Receive or use outside help. ・Sign your work with the honesty statement (provided on the website). ・Above all: You are here for yourself, practice makes perfection. 4 Organisational issues Presence: ・A presence sheet is circulated purely for statistics. ・Experience: those who do not attend lectures or do not make the assignments usually fail the course. -
Listing of Open Source Components
Last Updated: Friday, January 13, 2017 Manual Entry Version Listing of Open Source Components The following is a listing of open source licensed software which may accompany AMELIA®. Each of the components listed below may be redistributed under the terms of their respective open source licenses. Notwithstanding any of the terms in the license agreement you may have with IPsoft, the terms of such open source license(s) may be applicable to your use of the software identified below. Open Source Component Applicable License Antlr Tools BSD 3-Clause License Apache Core Apache Commons Apache Lucene Apache Tomcat Apache Struts Apache Mina/Mina SSHD Apache License 2.0 Apache Maria DB4j Apache HTTP Components Apache Groovy Apache Velocity Apache OpenNLP All Other Apache Libraries AspectJ weaver Eclipse Public License 1.0 Bean Validation API Apache License 2.0 Bouncy Castle Bouncy Castle License c3p0:JDBC DataSources/Resource Pools GNU General Lesser Public License 2.0 Camunda BPM Apache License 2.0 ClassMate Apache License 2.0 colt CERN Compiler Assisted Localization Library MIT License (CAL10N) API Core Hibernate O/RM functionality GNU General Lesser Public License 2.1 CRaSH by EXO GNU Lesser General Public License 2.1 Crfsuite for Java Apache License 2.0 cron4j GNU General Lesser Public License 3.0 Easy Rules Libraries MIT License Efficient Java Matrix Library Apache License 2.0 Ehcache Core Apache License 2.0 EmailValidator4J MIT License FasterXML Libraries Apache License 2.0 Flyway-core Apache License 2.0 Fortran to Java ARPACK BSD-2-Clause -
A Survey of Embeddings in Clinical Natural Language Processing Kalyan KS, S
This paper got published in Journal of Biomedical Informatics. Updated version is available @ https://doi.org/10.1016/j.jbi.2019.103323 SECNLP: A Survey of Embeddings in Clinical Natural Language Processing Kalyan KS, S. Sangeetha Text Analytics and Natural Language Processing Lab Department of Computer Applications National Institute of Technology Tiruchirappalli, INDIA. [email protected], [email protected] ABSTRACT Traditional representations like Bag of words are high dimensional, sparse and ignore the order as well as syntactic and semantic information. Distributed vector representations or embeddings map variable length text to dense fixed length vectors as well as capture prior knowledge which can transferred to downstream tasks. Even though embedding has become de facto standard for representations in deep learning based NLP tasks in both general and clinical domains, there is no survey paper which presents a detailed review of embeddings in Clinical Natural Language Processing. In this survey paper, we discuss various medical corpora and their characteristics, medical codes and present a brief overview as well as comparison of popular embeddings models. We classify clinical embeddings into nine types and discuss each embedding type in detail. We discuss various evaluation methods followed by possible solutions to various challenges in clinical embeddings. Finally, we conclude with some of the future directions which will advance the research in clinical embeddings. Keywords: embeddings, distributed representations, medical, natural language processing, survey 1. INTRODUCTION Distributed vector representation or embedding is one of the recent as well as prominent addition to modern natural language processing. Embedding has gained lot of attention and has become a part of NLP researcher’s toolkit. -
Calderon Carrión Eylin Dolores.Pdf
UNIVERSIDAD TÉCNICA PARTICULAR DE LOJA La Universidad Católica de Loja TITULACIÓN DE INGENIERO EN SISTEMAS INFORMÁTICOS Y COMPUTACIÓN Preprocesador de Planes Académicos de la UTPL Trabajo de fin de Titulación Autor: Calderón Carrión Eylin Dolores Director: Sucunuta España Manuel Eduardo, Ing. Loja – Ecuador 2012 CERTIFICACIÓN Ingeniero. Manuel Eduardo Sucunuta España DIRECTOR DEL TRABAJO DE FIN DE TITULACIÓN CERTIFICA: Que el presente trabajo, denominado: “Preprocesador de Planes Académicos" realizado por el profesional en formación: Eylin Dolores Calderón Carrión; cumple con los requisitos establecidos en las normas generales para la Graduación en la Universidad Técnica Particular de Loja, tanto en el aspecto de forma como de contenido, por lo cual me permito autorizar su presentación para los fines pertinentes. Loja, 22 de octubre de 2012 f) ……………………………………… CI: II CESIÓN DE DERECHOS Yo, Eylin Dolores Calderón Carrión declaro ser autora del presente trabajo y eximo expresamente a la Universidad Técnica Particular de Loja y a sus representantes legales de posibles reclamos o acciones legales. Adicionalmente declaro conocer y aceptar la disposición del Art. 67 del Estatuto Orgánico de la Universidad Técnica Particular de Loja que en su parte pertinente textualmente dice: “Forman parte del patrimonio de la Universidad la propiedad intelectual de investigaciones, trabajos científicos o técnicos y tesis de grado que se realicen a través, o con el apoyo financiero, académico o institucional (operativo) de la Universidad” f) ……………………………………… -
Open Source and Third Party Documentation
Open Source and Third Party Documentation Verint.com Twitter.com/verint Facebook.com/verint Blog.verint.com Content Introduction.....................2 Licenses..........................3 Page 1 Open Source Attribution Certain components of this Software or software contained in this Product (collectively, "Software") may be covered by so-called "free or open source" software licenses ("Open Source Components"), which includes any software licenses approved as open source licenses by the Open Source Initiative or any similar licenses, including without limitation any license that, as a condition of distribution of the Open Source Components licensed, requires that the distributor make the Open Source Components available in source code format. A license in each Open Source Component is provided to you in accordance with the specific license terms specified in their respective license terms. EXCEPT WITH REGARD TO ANY WARRANTIES OR OTHER RIGHTS AND OBLIGATIONS EXPRESSLY PROVIDED DIRECTLY TO YOU FROM VERINT, ALL OPEN SOURCE COMPONENTS ARE PROVIDED "AS IS" AND ANY EXPRESSED OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. Any third party technology that may be appropriate or necessary for use with the Verint Product is licensed to you only for use with the Verint Product under the terms of the third party license agreement specified in the Documentation, the Software or as provided online at http://verint.com/thirdpartylicense. You may not take any action that would separate the third party technology from the Verint Product. Unless otherwise permitted under the terms of the third party license agreement, you agree to only use the third party technology in conjunction with the Verint Product. -
Full-Graph-Limited-Mvn-Deps.Pdf
org.jboss.cl.jboss-cl-2.0.9.GA org.jboss.cl.jboss-cl-parent-2.2.1.GA org.jboss.cl.jboss-classloader-N/A org.jboss.cl.jboss-classloading-vfs-N/A org.jboss.cl.jboss-classloading-N/A org.primefaces.extensions.master-pom-1.0.0 org.sonatype.mercury.mercury-mp3-1.0-alpha-1 org.primefaces.themes.overcast-${primefaces.theme.version} org.primefaces.themes.dark-hive-${primefaces.theme.version}org.primefaces.themes.humanity-${primefaces.theme.version}org.primefaces.themes.le-frog-${primefaces.theme.version} org.primefaces.themes.south-street-${primefaces.theme.version}org.primefaces.themes.sunny-${primefaces.theme.version}org.primefaces.themes.hot-sneaks-${primefaces.theme.version}org.primefaces.themes.cupertino-${primefaces.theme.version} org.primefaces.themes.trontastic-${primefaces.theme.version}org.primefaces.themes.excite-bike-${primefaces.theme.version} org.apache.maven.mercury.mercury-external-N/A org.primefaces.themes.redmond-${primefaces.theme.version}org.primefaces.themes.afterwork-${primefaces.theme.version}org.primefaces.themes.glass-x-${primefaces.theme.version}org.primefaces.themes.home-${primefaces.theme.version} org.primefaces.themes.black-tie-${primefaces.theme.version}org.primefaces.themes.eggplant-${primefaces.theme.version} org.apache.maven.mercury.mercury-repo-remote-m2-N/Aorg.apache.maven.mercury.mercury-md-sat-N/A org.primefaces.themes.ui-lightness-${primefaces.theme.version}org.primefaces.themes.midnight-${primefaces.theme.version}org.primefaces.themes.mint-choc-${primefaces.theme.version}org.primefaces.themes.afternoon-${primefaces.theme.version}org.primefaces.themes.dot-luv-${primefaces.theme.version}org.primefaces.themes.smoothness-${primefaces.theme.version}org.primefaces.themes.swanky-purse-${primefaces.theme.version}