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biomedical NLP

Yanshan Wang, Stephen Wu, Dingcheng Li, Saeed Mehrabi, Hongfang Liu
In the era of digitalization, information retrieval (IR), which retrieves and ranks documents from large collections according to users' search queries, has been popularly applied in the biomedical domain. Building patient cohorts using electronic health records (EHRs) and searching literature for topics of interest are some IR use cases. Meanwhile, natural language processing (NLP), such as tokenization or Part-Of-Speech (POS) tagging, has been developed for processing clinical documents or biomedical literature...
September 1, 2016: Journal of Biomedical Informatics
Denis Griffis, Chaitanya Shivade, Eric Fosler-Lussier, Albert M Lai
Sentence boundary detection (SBD) is a critical preprocessing task for many natural language processing (NLP) applications. However, there has been little work on evaluating how well existing methods for SBD perform in the clinical domain. We evaluate five popular off-the-shelf NLP toolkits on the task of SBD in various kinds of text using a diverse set of corpora, including the GENIA corpus of biomedical abstracts, a corpus of clinical notes used in the 2010 i2b2 shared task, and two general-domain corpora (the British National Corpus and Switchboard)...
2016: AMIA Summits on Translational Science Proceedings
Kirk Roberts, Mary Regina Boland, Lisiane Pruinelli, Jina Dcruz, Andrew Berry, Mattias Georgsson, Rebecca Hazen, Raymond F Sarmiento, Uba Backonja, Kun-Hsing Yu, Yun Jiang, Patricia Flatley Brennan
The field of biomedical informatics experienced a productive 2015 in terms of research. In order to highlight the accomplishments of that research, elicit trends, and identify shortcomings at a macro level, a 19-person team conducted an extensive review of the literature in clinical and consumer informatics. The result of this process included a year-in-review presentation at the American Medical Informatics Association Annual Symposium and a written report (see supplemental data). Key findings are detailed in the report and summarized here...
August 7, 2016: Journal of the American Medical Informatics Association: JAMIA
Sean F Gilmore, Craig D Blanchette, Tiffany M Scharadin, Greg L Hura, Amy Rasley, Michele Corzett, Chong-Xian Pan, Nicholas O Fischer, Paul T Henderson
Nanolipoprotein particles (NLPs) consist of a discoidal phospholipid lipid bilayer confined by an apolipoprotein belt. NLPs are a promising platform for a variety of biomedical applications due to their biocompatibility, size, definable composition, and amphipathic characteristics. However, poor serum stability hampers the use of NLPs for in vivo applications such as drug formulation. In this study, NLP stability was enhanced upon the incorporation and subsequent UV-mediated intermolecular cross-linking of photoactive DiynePC phospholipids in the lipid bilayer, forming cross-linked nanoparticles (X-NLPs)...
August 17, 2016: ACS Applied Materials & Interfaces
Robin McEntire, Debbie Szalkowski, James Butler, Michelle S Kuo, Meiping Chang, Man Chang, Darren Freeman, Sarah McQuay, Jagruti Patel, Michael McGlashen, Wendy D Cornell, Jinghai James Xu
External content sources such as MEDLINE(®), National Institutes of Health (NIH) grants and conference websites provide access to the latest breaking biomedical information, which can inform pharmaceutical and biotechnology company pipeline decisions. The value of the sites for industry, however, is limited by the use of the public internet, the limited synonyms, the rarity of batch searching capability and the disconnected nature of the sites. Fortunately, many sites now offer their content for download and we have developed an automated internal workflow that uses text mining and tailored ontologies for programmatic search and knowledge extraction...
May 2016: Drug Discovery Today
Eugene Tseytlin, Kevin Mitchell, Elizabeth Legowski, Julia Corrigan, Girish Chavan, Rebecca S Jacobson
BACKGROUND: Natural language processing (NLP) applications are increasingly important in biomedical data analysis, knowledge engineering, and decision support. Concept recognition is an important component task for NLP pipelines, and can be either general-purpose or domain-specific. We describe a novel, flexible, and general-purpose concept recognition component for NLP pipelines, and compare its speed and accuracy against five commonly used alternatives on both a biological and clinical corpus...
2016: BMC Bioinformatics
Eugene Kolker, Imre Janko, Elizabeth Montague, Roger Higdon, Elizabeth Stewart, John Choiniere, Aaron Lai, Mary Eckert, William Broomall, Natali Kolker
Gene/disease associations are a critical part of exploring disease causes and ultimately cures, yet the publications that might provide such information are too numerous to be manually reviewed. We present a software utility, MOPED-Digger, that enables focused human assessment of literature by applying natural language processing (NLP) to search for customized lists of genes and diseases in titles and abstracts from biomedical publications. The results are ranked lists of gene/disease co-appearances and the publications that support them...
December 2015: Omics: a Journal of Integrative Biology
Pepi Sfakianaki, Lefteris Koumakis, Stelios Sfakianakis, Galatia Iatraki, Giorgos Zacharioudakis, Norbert Graf, Kostas Marias, Manolis Tsiknakis
BACKGROUND: A plethora of publicly available biomedical resources do currently exist and are constantly increasing at a fast rate. In parallel, specialized repositories are been developed, indexing numerous clinical and biomedical tools. The main drawback of such repositories is the difficulty in locating appropriate resources for a clinical or biomedical decision task, especially for non-Information Technology expert users. In parallel, although NLP research in the clinical domain has been active since the 1960s, progress in the development of NLP applications has been slow and lags behind progress in the general NLP domain...
2015: BMC Medical Informatics and Decision Making
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
Thierry Hamon, Fleur Mougin, Natalia Grabar
With the recent and intensive research in the biomedical area, the knowledge accumulated is disseminated through various knowledge bases. Links between these knowledge bases are needed in order to use them jointly. Linked Data, SPARQL language, and interfaces in Natural Language question-answering provide interesting solutions for querying such knowledge bases. We propose a method for translating natural language questions in SPARQL queries. We use Natural Language Processing tools, semantic resources, and the RDF triples description...
2015: Studies in Health Technology and Informatics
Kai Zheng, V G Vinod Vydiswaran, Yang Liu, Yue Wang, Amber Stubbs, Özlem Uzuner, Anupama E Gururaj, Samuel Bayer, John Aberdeen, Anna Rumshisky, Serguei Pakhomov, Hongfang Liu, Hua Xu
OBJECTIVE: In recognition of potential barriers that may inhibit the widespread adoption of biomedical software, the 2014 i2b2 Challenge introduced a special track, Track 3 - Software Usability Assessment, in order to develop a better understanding of the adoption issues that might be associated with the state-of-the-art clinical NLP systems. This paper reports the ease of adoption assessment methods we developed for this track, and the results of evaluating five clinical NLP system submissions...
December 2015: Journal of Biomedical Informatics
Ioannis Korkontzelos, Dimitrios Piliouras, Andrew W Dowsey, Sophia Ananiadou
OBJECTIVE: Drug named entity recognition (NER) is a critical step for complex biomedical NLP tasks such as the extraction of pharmacogenomic, pharmacodynamic and pharmacokinetic parameters. Large quantities of high quality training data are almost always a prerequisite for employing supervised machine-learning techniques to achieve high classification performance. However, the human labour needed to produce and maintain such resources is a significant limitation. In this study, we improve the performance of drug NER without relying exclusively on manual annotations...
October 2015: Artificial Intelligence in Medicine
Yang Chen, Li Li, Guo-Qiang Zhang, Rong Xu
MOTIVATION: Discerning genetic contributions to diseases not only enhances our understanding of disease mechanisms, but also leads to translational opportunities for drug discovery. Recent computational approaches incorporate disease phenotypic similarities to improve the prediction power of disease gene discovery. However, most current studies used only one data source of human disease phenotype. We present an innovative and generic strategy for combining multiple different data sources of human disease phenotype and predicting disease-associated genes from integrated phenotypic and genomic data...
June 15, 2015: Bioinformatics
Joshua C Denny, Anderson Spickard, Peter J Speltz, Renee Porier, Donna E Rosenstiel, James S Powers
OBJECTIVE: Assessment of medical trainee learning through pre-defined competencies is now commonplace in schools of medicine. We describe a novel electronic advisor system using natural language processing (NLP) to identify two geriatric medicine competencies from medical student clinical notes in the electronic medical record: advance directives (AD) and altered mental status (AMS). MATERIALS AND METHODS: Clinical notes from third year medical students were processed using a general-purpose NLP system to identify biomedical concepts and their section context...
August 2015: Journal of Biomedical Informatics
Peggy Cellier, Thierry Charnois, Marc Plantevit, Christophe Rigotti, Bruno Crémilleux, Olivier Gandrillon, Jiří Kléma, Jean-Luc Manguin
BACKGROUND: Discovering gene interactions and their characterizations from biological text collections is a crucial issue in bioinformatics. Indeed, text collections are large and it is very difficult for biologists to fully take benefit from this amount of knowledge. Natural Language Processing (NLP) methods have been applied to extract background knowledge from biomedical texts. Some of existing NLP approaches are based on handcrafted rules and thus are time consuming and often devoted to a specific corpus...
2015: Journal of Biomedical Semantics
Yang Liu, Songhua Xu, Hong-Jun Yoon, Georgia Tourassi
Natural language processing has been successfully leveraged to extract patient information from unstructured clinical text. However the majority of the existing work targets at obtaining a specific category of clinical information through individual efforts. In the midst of the Health 2.0 wave, online health forums increasingly host abundant and diverse health-related information regarding the demographics and medical information of patients who are either actively participating in or passively reported at these forums...
2014: AMIA ... Annual Symposium Proceedings
Hyeoneui Kim, Lucila Ohno-Machado, Janet Oh, Xiaoqian Jiang
We analyzed 741 journal articles on nursing informatics published in 7 biomedical/nursing informatics journals and 6 nursing journals from 2005 to 2013 to begin to understand publication trends in nursing informatics research and identify gaps. We assigned a research theme to each article using AMIA 2014 theme categories and normalized the citation counts using time from publication. Overall, nursing informatics research covered a broad spectrum of research topics in biomedical informatics and publication topics seem to be well aligned with the high priority research agenda identified by the nursing informatics community...
2014: AMIA ... Annual Symposium Proceedings
Richard G Jackson MSc, Michael Ball, Rashmi Patel, Richard D Hayes, Richard J B Dobson, Robert Stewart
Observational research using data from electronic health records (EHR) is a rapidly growing area, which promises both increased sample size and data richness - therefore unprecedented study power. However, in many medical domains, large amounts of potentially valuable data are contained within the free text clinical narrative. Manually reviewing free text to obtain desired information is an inefficient use of researcher time and skill. Previous work has demonstrated the feasibility of applying Natural Language Processing (NLP) to extract information...
2014: AMIA ... Annual Symposium Proceedings
Chung-Chi Huang, Zhiyong Lu
One effective way to improve the state of the art is through competitions. Following the success of the Critical Assessment of protein Structure Prediction (CASP) in bioinformatics research, a number of challenge evaluations have been organized by the text-mining research community to assess and advance natural language processing (NLP) research for biomedicine. In this article, we review the different community challenge evaluations held from 2002 to 2014 and their respective tasks. Furthermore, we examine these challenge tasks through their targeted problems in NLP research and biomedical applications, respectively...
January 2016: Briefings in Bioinformatics
Parisa Kordjamshidi, Dan Roth, Marie-Francine Moens
BACKGROUND: We aim to automatically extract species names of bacteria and their locations from webpages. This task is important for exploiting the vast amount of biological knowledge which is expressed in diverse natural language texts and putting this knowledge in databases for easy access by biologists. The task is challenging and the previous results are far below an acceptable level of performance, particularly for extraction of localization relationships. Therefore, we aim to design a new system for such extractions, using the framework of structured machine learning techniques...
2015: BMC Bioinformatics
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