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https://www.readbyqxmd.com/read/29901703/metamap-an-atlas-of-metatranscriptomic-reads-in-human-disease-related-rna-seq-data
#1
L M Simon, S Karg, A J Westermann, M Engel, A H A Elbehery, B Hense, M Heinig, L Deng, F J Theis
Background: With the advent of the age of big data in bioinformatics, large volumes of data and high performance computing power enable researchers to perform re-analyses of publicly available datasets at an unprecedented scale. Ever more studies imply the microbiome in both normal human physiology and a wide range of diseases. RNA sequencing technology (RNA-seq) is commonly used to infer global eukaryotic gene expression patterns under defined conditions, including human disease-related contexts, but its generic nature also enables the detection of microbial and viral transcripts...
June 12, 2018: GigaScience
https://www.readbyqxmd.com/read/29895320/disease-mentions-in-airport-and-hospital-geolocations-expose-dominance-of-news-events-for-disease-concerns
#2
Joana M Barros, Jim Duggan, Dietrich Rebholz-Schuhmann
BACKGROUND: In recent years, Twitter has been applied to monitor diseases through its facility to monitor users' comments and concerns in real-time. The analysis of tweets for disease mentions should reflect not only user specific concerns but also disease outbreaks. This requires the use of standard terminological resources and can be focused on selected geographic locations. In our study, we differentiate between hospital and airport locations to better distinguish disease outbreaks from background mentions of disease concerns...
June 12, 2018: Journal of Biomedical Semantics
https://www.readbyqxmd.com/read/29860093/evaluation-of-natural-language-processing-nlp-systems-to-annotate-drug-product-labeling-with-meddra-terminology
#3
Thomas Ly, Carol Pamer, Oanh Dang, Sonja Brajovic, Shahrukh Haider, Taxiarchis Botsis, David Milward, Andrew Winter, Susan Lu, Robert Ball
INTRODUCTION: The FDA Adverse Event Reporting System (FAERS) is a primary data source for identifying unlabeled adverse events (AEs) in a drug or biologic drug product's postmarketing phase. Many AE reports must be reviewed by drug safety experts to identify unlabeled AEs, even if the reported AEs are previously identified, labeled AEs. Integrating the labeling status of drug product AEs into FAERS could increase report triage and review efficiency. Medical Dictionary for Regulatory Activities (MedDRA) is the standard for coding AE terms in FAERS cases...
May 31, 2018: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29730047/leveraging-wikipedia-knowledge-to-classify-multilingual-biomedical-documents
#4
Marcos Antonio Mouriño García, Roberto Pérez Rodríguez, Luis Anido Rifón
This article presents a classifier that leverages Wikipedia knowledge to represent documents as vectors of concepts weights, and analyses its suitability for classifying biomedical documents written in any language when it is trained only with English documents. We propose the cross-language concept matching technique, which relies on Wikipedia interlanguage links to convert concept vectors between languages. The performance of the classifier is compared to a classifier based on machine translation, and two classifiers based on MetaMap...
May 2, 2018: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/29500013/applying-natural-language-processing-techniques-to-develop-a-task-specific-emr-interface-for-timely-stroke-thrombolysis-a-feasibility-study
#5
Sheng-Feng Sung, Kuanchin Chen, Darren Philbert Wu, Ling-Chien Hung, Yu-Hsiang Su, Ya-Han Hu
OBJECTIVE: To reduce errors in determining eligibility for intravenous thrombolytic therapy (IVT) in stroke patients through use of an enhanced task-specific electronic medical record (EMR) interface powered by natural language processing (NLP) techniques. MATERIALS AND METHODS: The information processing algorithm utilized MetaMap to extract medical concepts from IVT eligibility criteria and expanded the concepts using the Unified Medical Language System Metathesaurus...
April 2018: International Journal of Medical Informatics
https://www.readbyqxmd.com/read/29399672/knowledge-based-biomedical-word-sense-disambiguation-with-neural-concept-embeddings
#6
Akm Sabbir, Antonio Jimeno-Yepes, Ramakanth Kavuluru
Biomedical word sense disambiguation (WSD) is an important intermediate task in many natural language processing applications such as named entity recognition, syntactic parsing, and relation extraction. In this paper, we employ knowledge-based approaches that also exploit recent advances in neural word/concept embeddings to improve over the state-of-the-art in biomedical WSD using the public MSH WSD dataset [1] as the test set. Our methods involve weak supervision - we do not use any hand-labeled examples for WSD to build our prediction models; however, we employ an existing concept mapping program, MetaMap, to obtain our concept vectors...
October 2017: Proceedings
https://www.readbyqxmd.com/read/29247788/vector-representations-of-multi-word-terms-for-semantic-relatedness
#7
Sam Henry, Clint Cuffy, Bridget T McInnes
This paper presents a comparison between several multi-word term aggregation methods of distributional context vectors applied to the task of semantic similarity and relatedness in the biomedical domain. We compare the multi-word term aggregation methods of summation of component word vectors, mean of component word vectors, direct construction of compound term vectors using the compoundify tool, and direct construction of concept vectors using the MetaMap tool. Dimensionality reduction is critical when constructing high quality distributional context vectors, so these baseline co-occurrence vectors are compared against dimensionality reduced vectors created using singular value decomposition (SVD), and word2vec word embeddings using continuous bag of words (CBOW), and skip-gram models...
January 2018: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29186491/clamp-a-toolkit-for-efficiently-building-customized-clinical-natural-language-processing-pipelines
#8
Ergin Soysal, Jingqi Wang, Min Jiang, Yonghui Wu, Serguei Pakhomov, Hongfang Liu, Hua Xu
Existing general clinical natural language processing (NLP) systems such as MetaMap and Clinical Text Analysis and Knowledge Extraction System have been successfully applied to information extraction from clinical text. However, end users often have to customize existing systems for their individual tasks, which can require substantial NLP skills. Here we present CLAMP (Clinical Language Annotation, Modeling, and Processing), a newly developed clinical NLP toolkit that provides not only state-of-the-art NLP components, but also a user-friendly graphic user interface that can help users quickly build customized NLP pipelines for their individual applications...
November 24, 2017: Journal of the American Medical Informatics Association: JAMIA
https://www.readbyqxmd.com/read/28986329/detecting-clinically-related-content-in-online-patient-posts
#9
Courtland VanDam, Shaheen Kanthawala, Wanda Pratt, Joyce Chai, Jina Huh
Patients with chronic health conditions use online health communities to seek support and information to help manage their condition. For clinically related topics, patients can benefit from getting opinions from clinical experts, and many are concerned about misinformation and biased information being spread online. However, a large volume of community posts makes it challenging for moderators and clinical experts, if there are any, to provide necessary information. Automatically identifying forum posts that need validated clinical resources can help online health communities efficiently manage content exchange...
November 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28883198/analysis-of-annotated-data-models-for-improving-data-quality
#10
Hannes Ulrich, Ann-Kristin Kock-Schoppenhauer, Björn Andersen, Josef Ingenerf
The public Medical Data Models (MDM) portal with more than 9.000 annotated forms from clinical trials and other sources provides many research opportunities for the medical informatics community. It is mainly used to address the problem of heterogeneity by searching, mediating, reusing, and assessing data models, e. g. the semi-interactive curation of core data records in a special domain. Furthermore, it can be used as a benchmark for evaluating algorithms that create, transform, annotate, and analyse structured patient data...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28816337/a-bag-of-concepts-approach-for-biomedical-document-classification-using-wikipedia-knowledge-spanish-english-cross-language-case-study
#11
Marcos A Mouriño-García, Roberto Pérez-Rodríguez, Luis E Anido-Rifón
OBJECTIVES: The ability to efficiently review the existing literature is essential for the rapid progress of research. This paper describes a classifier of text documents, represented as vectors in spaces of Wikipedia concepts, and analyses its suitability for classification of Spanish biomedical documents when only English documents are available for training. We propose the cross-language concept matching (CLCM) technique, which relies on Wikipedia interlanguage links to convert concept vectors from the Spanish to the English space...
October 26, 2017: Methods of Information in Medicine
https://www.readbyqxmd.com/read/28815108/monitoring-biomedical-literature-for-post-market-safety-purposes-by-analyzing-networks-of-text-based-coded-information
#12
Taxiarchis Botsis, Matthew Foster, Kory Kreimeyer, Abhishek Pandey, Richard Forshee
Literature review is critical but time-consuming in the post-market surveillance of medical products. We focused on the safety signal of intussusception after the vaccination of infants with the Rotashield Vaccine in 1999 and retrieved all PubMed abstracts for rotavirus vaccines published after January 1, 1998. We used the Event-based Text-mining of Health Electronic Records system, the MetaMap tool, and the National Center for Biomedical Ontologies Annotator to process the abstracts and generate coded terms stamped with the date of publication...
2017: AMIA Summits on Translational Science Proceedings
https://www.readbyqxmd.com/read/28664200/an-ontology-enabled-natural-language-processing-pipeline-for-provenance-metadata-extraction-from-biomedical-text-short-paper
#13
Joshua Valdez, Michael Rueschman, Matthew Kim, Susan Redline, Satya S Sahoo
Extraction of structured information from biomedical literature is a complex and challenging problem due to the complexity of biomedical domain and lack of appropriate natural language processing (NLP) techniques. High quality domain ontologies model both data and metadata information at a fine level of granularity, which can be effectively used to accurately extract structured information from biomedical text. Extraction of provenance metadata, which describes the history or source of information, from published articles is an important task to support scientific reproducibility...
October 2016: On the Move to Meaningful Internet Systems ...: CoopIS, DOA, and ODBASE: Confederated International Conferences, CoopIS, DOA, and ODBASE ... Proceedings
https://www.readbyqxmd.com/read/28552401/rysannmd-a-biomedical-semantic-annotator-balancing-speed-and-accuracy
#14
John Cuzzola, Jelena Jovanović, Ebrahim Bagheri
Recently, both researchers and practitioners have explored the possibility of semantically annotating large and continuously evolving collections of biomedical texts such as research papers, medical reports, and physician notes in order to enable their efficient and effective management and use in clinical practice or research laboratories. Such annotations can be automatically generated by biomedical semantic annotators - tools that are specifically designed for detecting and disambiguating biomedical concepts mentioned in text...
July 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28391809/leveraging-medical-taxonomies-to-improve-knowledge-management-within-online-communities-of-practice-the-knowledge-maps-system
#15
Samuel Alan Stewart, Syed Sibte Raza Abidi
BACKGROUND AND OBJECTIVE: Online communities of practice contain a wealth of information, stored in the free text of shared communications between community members. The Knowledge Maps (KMaps) system is designed to facilitate Knowledge Translation in online communities through multi-level analyses of the shared messages of these communications. METHODS: Using state-of-the-art semantic mapping technologies (Metamap) the contents of the messages shared within an online community are mapped to terms from the MeSH medical lexicon, providing a multi-level topic-specific summary of the knowledge being shared within the community...
May 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28337503/gut-microbiome-related-metabolic-changes-in-plasma-of-antibiotic-treated-rats
#16
C Behr, H Kamp, E Fabian, G Krennrich, W Mellert, E Peter, V Strauss, T Walk, I M C M Rietjens, B van Ravenzwaay
The intestinal microbiota contributes to the metabolism of its host. Adequate identification of the microbiota's impact on the host plasma metabolites is lacking. As antibiotics have a profound effect on the microbial composition and hence on the mammalian-microbiota co-metabolism, we studied the effects of antibiotics on the "functionality of the microbiome"-defined as the production of metabolites absorbed by the host. This metabolomics study presents insights into the mammalian-microbiome co-metabolism of endogenous metabolites...
March 23, 2017: Archives of Toxicology
https://www.readbyqxmd.com/read/28131722/development-and-empirical-user-centered-evaluation-of-semantically-based-query-recommendation-for-an-electronic-health-record-search-engine
#17
David A Hanauer, Danny T Y Wu, Lei Yang, Qiaozhu Mei, Katherine B Murkowski-Steffy, V G Vinod Vydiswaran, Kai Zheng
OBJECTIVE: The utility of biomedical information retrieval environments can be severely limited when users lack expertise in constructing effective search queries. To address this issue, we developed a computer-based query recommendation algorithm that suggests semantically interchangeable terms based on an initial user-entered query. In this study, we assessed the value of this approach, which has broad applicability in biomedical information retrieval, by demonstrating its application as part of a search engine that facilitates retrieval of information from electronic health records (EHRs)...
March 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28130331/metamap-lite-an-evaluation-of-a-new-java-implementation-of-metamap
#18
COMPARATIVE STUDY
Dina Demner-Fushman, Willie J Rogers, Alan R Aronson
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...
July 1, 2017: Journal of the American Medical Informatics Association: JAMIA
https://www.readbyqxmd.com/read/27903489/finding-important-terms-for-patients-in-their-electronic-health-records-a-learning-to-rank-approach-using-expert-annotations
#19
Jinying Chen, Jiaping Zheng, Hong Yu
BACKGROUND: Many health organizations allow patients to access their own electronic health record (EHR) notes through online patient portals as a way to enhance patient-centered care. However, EHR notes are typically long and contain abundant medical jargon that can be difficult for patients to understand. In addition, many medical terms in patients' notes are not directly related to their health care needs. One way to help patients better comprehend their own notes is to reduce information overload and help them focus on medical terms that matter most to them...
November 30, 2016: JMIR Medical Informatics
https://www.readbyqxmd.com/read/27683667/v3nlp-framework-tools-to-build-applications-for-extracting-concepts-from-clinical-text
#20
Guy Divita, Marjorie E Carter, Le-Thuy Tran, Doug Redd, Qing T Zeng, Scott Duvall, Matthew H Samore, Adi V Gundlapalli
INTRODUCTION: Substantial amounts of clinically significant information are contained only within the narrative of the clinical notes in electronic medical records. The v3NLP Framework is a set of "best-of-breed" functionalities developed to transform this information into structured data for use in quality improvement, research, population health surveillance, and decision support. BACKGROUND: MetaMap, cTAKES and similar well-known natural language processing (NLP) tools do not have sufficient scalability out of the box...
2016: EGEMS
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