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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)...
January 25, 2017: Journal of Biomedical Informatics
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...
January 27, 2017: Journal of the American Medical Informatics Association: JAMIA
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
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
Emma Chiaramello, Alessia Paglialonga, Francesco Pinciroli, Gabriella Tognola
This study assessed the feasibility of using MetaMap to identify medical concepts from clinical notes written in Italian. We performed two experiments: in "EXP 1", we used MetaMap to annotate Italian texts using a knowledge source consisting of Italian UMLS sources only; in "EXP 2", we used MetaMap to analyze an English unsupervised translated version of the original Italian texts. We considered medical concepts related to three semantic categories: "Disorders", "Findings" and "Symptoms". Average recall, precision and F-measure were equal to 0...
2016: Studies in Health Technology and Informatics
Yonghui Wu, Joshua C Denny, S Trent Rosenbloom, Randolph A Miller, Dario A Giuse, Lulu Wang, Carmelo Blanquicett, Ergin Soysal, Jun Xu, Hua Xu
OBJECTIVE: The goal of this study was to develop a practical framework for recognizing and disambiguating clinical abbreviations, thereby improving current clinical natural language processing (NLP) systems' capability to handle abbreviations in clinical narratives. METHODS: We developed an open-source framework for clinical abbreviation recognition and disambiguation (CARD) that leverages our previously developed methods, including: (1) machine learning based approaches to recognize abbreviations from a clinical corpus, (2) clustering-based semiautomated methods to generate possible senses of abbreviations, and (3) profile-based word sense disambiguation methods for clinical abbreviations...
August 18, 2016: Journal of the American Medical Informatics Association: JAMIA
Emma Chiaramello, Francesco Pinciroli, Alberico Bonalumi, Angelo Caroli, Gabriella Tognola
Information extraction from narrative clinical notes is useful for patient care, as well as for secondary use of medical data, for research or clinical purposes. Many studies focused on information extraction from English clinical texts, but less dealt with clinical notes in languages other than English. This study tested the feasibility of using "off the shelf" information extraction algorithms to identify medical concepts from Italian clinical notes. Among all the available and well-established information extraction algorithms, we used MetaMap to map medical concepts to the Unified Medical Language System (UMLS)...
July 18, 2016: Journal of Biomedical Informatics
Calvin Lam, Fu-Chih Lai, Chia-Hui Wang, Mei-Hsin Lai, Nanly Hsu, Min-Huey Chung
OBJECTIVE: Research on publication trends in journal articles on sleep disorders (SDs) and the associated methodologies by using text mining has been limited. The present study involved text mining for terms to determine the publication trends in sleep-related journal articles published during 2000-2013 and to identify associations between SD and methodology terms as well as conducting statistical analyses of the text mining findings. METHODS: SD and methodology terms were extracted from 3,720 sleep-related journal articles in the PubMed database by using MetaMap...
2016: PloS One
E Fabian, N Bordag, M Herold, H Kamp, G Krennrich, R Looser, L Ma-Hock, W Mellert, G Montoya, E Peter, A Prokudin, M Spitzer, V Strauss, T Walk, R Zbranek, B van Ravenzwaay
The MetaMap(®)-Tox database contains plasma-metabolome and toxicity data of rats obtained from oral administration of 550 reference compounds following a standardized adapted OECD 407 protocol. Here, metabolic profiles for aniline (A), chloroform (CL), ethylbenzene (EB), 2-methoxyethanol (ME), N,N-dimethylformamide (DMF) and tetrahydrofurane (THF), dosed inhalatively for six hours/day, five days a week for 4 weeks were compared to oral dosing performed daily for 4 weeks. To investigate if the oral and inhalative metabolome would be comparable statistical analyses were performed...
July 25, 2016: Toxicology Letters
John D Osborne, Matthew Wyatt, Andrew O Westfall, James Willig, Steven Bethard, Geoff Gordon
OBJECTIVE: To help cancer registrars efficiently and accurately identify reportable cancer cases. MATERIAL AND METHODS: The Cancer Registry Control Panel (CRCP) was developed to detect mentions of reportable cancer cases using a pipeline built on the Unstructured Information Management Architecture - Asynchronous Scaleout (UIMA-AS) architecture containing the National Library of Medicine's UIMA MetaMap annotator as well as a variety of rule-based UIMA annotators that primarily act to filter out concepts referring to nonreportable cancers...
November 2016: Journal of the American Medical Informatics Association: JAMIA
Albert Park, Andrea L Hartzler, Jina Huh, David W McDonald, Wanda Pratt
BACKGROUND: The prevalence and value of patient-generated health text are increasing, but processing such text remains problematic. Although existing biomedical natural language processing (NLP) tools are appealing, most were developed to process clinician- or researcher-generated text, such as clinical notes or journal articles. In addition to being constructed for different types of text, other challenges of using existing NLP include constantly changing technologies, source vocabularies, and characteristics of text...
2015: Journal of Medical Internet Research
Yaoyun Zhang, Ergin Soysal, Sungrim Moon, Jingqi Wang, Cui Tao, Hua Xu
A computable knowledge base containing relations between diseases and lab tests would be a great resource for many biomedical informatics applications. This paper describes our initial step towards establishing a comprehensive knowledge base of disease and lab tests relations utilizing three public on-line resources. LabTestsOnline, MedlinePlus and Wikipedia are integrated to create a freely available, computable disease-lab test knowledgebase. Disease and lab test concepts are identified using MetaMap and relations between diseases and lab tests are determined based on source-specific rules...
2015: AMIA Summits on Translational Science Proceedings
David T Marc, Rui Zhang, James Beattie, Laël C Gatewood, Saif S Khairat
As part of the Open Government Initiative, the United States federal government published datasets to increase collaboration, transparency, consumer participation, and research, and are available online at Currently, does not adequately support the accessibility goal of the Open Government Initiative due to issues of retrieving relevant data because of inadequately cataloguing and lack of indexing with a standardized terminology. Given the commonalities between the HealthData...
2015: Studies in Health Technology and Informatics
Rohit J Kate
BACKGROUND: Variations of clinical terms are very commonly encountered in clinical texts. Normalization methods that use similarity measures or hand-coded approximation rules for matching clinical terms to standard terminologies have limited accuracy and coverage. MATERIALS AND METHODS: In this paper, a novel method is presented that automatically learns patterns of variations of clinical terms from known variations from a resource such as the Unified Medical Language System (UMLS)...
March 2016: Journal of the American Medical Informatics Association: JAMIA
Ashutosh Jadhav, Amit Sheth, Jyotishman Pathak
Since the early 2000's, Internet usage for health information searching has increased significantly. Studying search queries can help us to understand users "information need" and how do they formulate search queries ("expression of information need"). Although cardiovascular diseases (CVD) affect a large percentage of the population, few studies have investigated how and what users search for CVD. We address this knowledge gap in the community by analyzing a large corpus of 10 million CVD related search queries from MayoClinic...
2014: AMIA ... Annual Symposium Proceedings
Guy Divita, Qing T Zeng, Adi V Gundlapalli, Scott Duvall, Jonathan Nebeker, Matthew H Samore
An opportunity exists for meaningful concept extraction and indexing from large corpora of clinical notes in the Veterans Affairs (VA) electronic medical record. Currently available tools such as MetaMap, cTAKES and HITex do not scale up to address this big data need. Sophia, a rapid UMLS concept extraction annotator was developed to fulfill a mandate and address extraction where high throughput is needed while preserving performance. We report on the development, testing and benchmarking of Sophia against MetaMap and cTAKEs...
2014: AMIA ... Annual Symposium Proceedings
Hanna Suominen, Liyuan Zhou, Leif Hanlen, Gabriela Ferraro
BACKGROUND: Over a tenth of preventable adverse events in health care are caused by failures in information flow. These failures are tangible in clinical handover; regardless of good verbal handover, from two-thirds to all of this information is lost after 3-5 shifts if notes are taken by hand, or not at all. Speech recognition and information extraction provide a way to fill out a handover form for clinical proofing and sign-off. OBJECTIVE: The objective of the study was to provide a recorded spoken handover, annotated verbatim transcriptions, and evaluations to support research in spoken and written natural language processing for filling out a clinical handover form...
2015: JMIR Medical Informatics
Antonio Jose Jimeno Yepes, Laura Plaza, Jorge Carrillo-de-Albornoz, James G Mork, Alan R Aronson
BACKGROUND: Research in biomedical text categorization has mostly used the bag-of-words representation. Other more sophisticated representations of text based on syntactic, semantic and argumentative properties have been less studied. In this paper, we evaluate the impact of different text representations of biomedical texts as features for reproducing the MeSH annotations of some of the most frequent MeSH headings. In addition to unigrams and bigrams, these features include noun phrases, citation meta-data, citation structure, and semantic annotation of the citations...
April 8, 2015: BMC Bioinformatics
Anika Oellrich, Nigel Collier, Damian Smedley, Tudor Groza
Electronic health records and scientific articles possess differing linguistic characteristics that may impact the performance of natural language processing tools developed for one or the other. In this paper, we investigate the performance of four extant concept recognition tools: the clinical Text Analysis and Knowledge Extraction System (cTAKES), the National Center for Biomedical Ontology (NCBO) Annotator, the Biomedical Concept Annotation System (BeCAS) and MetaMap. Each of the four concept recognition systems is applied to four different corpora: the i2b2 corpus of clinical documents, a PubMed corpus of Medline abstracts, a clinical trails corpus and the ShARe/CLEF corpus...
2015: PloS One
Antonio Jimeno Yepes, Rafael Berlanga
Text mining of scientific literature has been essential for setting up large public biomedical databases, which are being widely used by the research community. In the biomedical domain, the existence of a large number of terminological resources and knowledge bases (KB) has enabled a myriad of machine learning methods for different text mining related tasks. Unfortunately, KBs have not been devised for text mining tasks but for human interpretation, thus performance of KB-based methods is usually lower when compared to supervised machine learning methods...
February 2015: Journal of Biomedical Informatics
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