3,825 research outputs found
Utilizing RxNorm to Support Practical Computing Applications: Capturing Medication History in Live Electronic Health Records
RxNorm was utilized as the basis for direct-capture of medication history
data in a live EHR system deployed in a large, multi-state outpatient
behavioral healthcare provider in the United States serving over 75,000
distinct patients each year across 130 clinical locations. This tool
incorporated auto-complete search functionality for medications and proper
dosage identification assistance. The overarching goal was to understand if and
how standardized terminologies like RxNorm can be used to support practical
computing applications in live EHR systems. We describe the stages of
implementation, approaches used to adapt RxNorm's data structure for the
intended EHR application, and the challenges faced. We evaluate the
implementation using a four-factor framework addressing flexibility, speed,
data integrity, and medication coverage. RxNorm proved to be functional for the
intended application, given appropriate adaptations to address high-speed
input/output (I/O) requirements of a live EHR and the flexibility required for
data entry in multiple potential clinical scenarios. Future research around
search optimization for medication entry, user profiling, and linking RxNorm to
drug classification schemes holds great potential for improving the user
experience and utility of medication data in EHRs.Comment: Appendix (including SQL/DDL Code) available by author request.
Keywords: RxNorm; Electronic Health Record; Medication History;
Interoperability; Unified Medical Language System; Search Optimizatio
Issues in the Design of a Pilot Concept-Based Query Interface for the Neuroinformatics Information Framework
This paper describes a pilot query interface that has been constructed to help us explore a "concept-based" approach for searching the
Neuroscience Information Framework (NIF). The query interface is
concept-based in the sense that the search terms submitted through the
interface are selected from a standardized vocabulary of terms
(concepts) that are structured in the form of an ontology. The NIF
contains three primary resources: the NIF Resource Registry, the NIF
Document Archive, and the NIF Database Mediator. These NIF resources
are very different in their nature and therefore pose challenges when
designing a single interface from which searches can be automatically
launched against all three resources simultaneously. The paper first
discusses briefly several background issues involving the use of
standardized biomedical vocabularies in biomedical information
retrieval, and then presents a detailed example that illustrates how
the pilot concept-based query interface operates. The paper concludes
by discussing certain lessons learned in the development of the current
version of the interface
Information retrieval and text mining technologies for chemistry
Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European
Community’s Horizon 2020 Program (project reference:
654021 - OpenMinted). M.K. additionally acknowledges the
Encomienda MINETAD-CNIO as part of the Plan for the
Advancement of Language Technology. O.R. and J.O. thank
the Foundation for Applied Medical Research (FIMA),
University of Navarra (Pamplona, Spain). This work was
partially funded by Consellería
de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic
funding of UID/BIO/04469/2013 unit and COMPETE 2020
(POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi
for useful feedback and discussions during the preparation of
the manuscript.info:eu-repo/semantics/publishedVersio
ClassyFire: automated chemical classification with a comprehensive, computable taxonomy
Additional file 5. Use cases. Text-based search on the ClassyFire web server. (A) Building the query. (B) Sparteine, one of the returned compounds
The BioGRID Interaction Database: 2011 update
The Biological General Repository for Interaction Datasets (BioGRID) is a public database that archives and disseminates genetic and protein
interaction data from model organisms and humans
(http://www.thebiogrid.org). BioGRID currently holds 347 966
interactions (170 162 genetic, 177 804 protein) curated from both
high-throughput data sets and individual focused studies, as derived
from over 23 000 publications in the primary literature. Complete
coverage of the entire literature is maintained for budding yeast
(Saccharomyces cerevisiae), fission yeast (Schizosaccharomyces pombe)
and thale cress (Arabidopsis thaliana), and efforts to expand curation
across multiple metazoan species are underway. The BioGRID houses 48
831 human protein interactions that have been curated from 10 247
publications. Current curation drives are focused on particular areas
of biology to enable insights into conserved networks and pathways that
are relevant to human health. The BioGRID 3.0 web interface contains
new search and display features that enable rapid queries across
multiple data types and sources. An automated Interaction Management
System (IMS) is used to prioritize, coordinate and track curation
across international sites and projects. BioGRID provides interaction
data to several model organism databases, resources such as Entrez-Gene
and other interaction meta-databases. The entire BioGRID 3.0 data
collection may be downloaded in multiple file formats, including PSI MI
XML. Source code for BioGRID 3.0 is freely available without any
restrictions
A system for automated lexical mapping
Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2005.Includes bibliographical references (leaves 19-20).Merging of clinical systems and medical databases, or aggregation of information from disparate databases, frequently requires a process where vocabularies are compared and similar concepts are mapped. Using a normalization phase followed by a novel alignment stage inspired by DNA sequence alignment methods, automated lexical mapping can map terms from various databases to standard vocabularies such as UMLS (Unified Medical Language System) and SNOMED (the Systematized Nomenclature of Medicine). This automated lexical mapping was evaluated using a real-world database of consultation letters from Children's Hospital Boston. The first phase involved extracting the reason for referral from the consultation letters. The reasons for referral were then mapped to SNOMED. The alignment algorithm was able to map 72% of equivalent concepts through lexical mapping alone. Lexical mapping can facilitate the integration of data from diverse sources and decrease the time and cost required for manual mapping and integration of clinical systems and medical databases.by Jennifer Y. Sun.S.M
Terminology Services: Standard Terminologies to Control Medical Vocabulary. “Words are Not What they Say but What they Mean”
Data entry is an obstacle for the usability of electronic health records (EHR) applications and the acceptance of physicians, who prefer to document using “free text”. Natural language is huge and very rich in details but at the same time is ambiguous; it has great dependence on context and uses jargon and acronyms. Healthcare Information Systems should capture clinical data in a structured and preferably coded format. This is crucial for data exchange between health information systems, epidemiological analysis, quality and research, clinical decision support systems, administrative functions, etc. In order to address this point, numerous terminological systems for the systematic recording of clinical data have been developed. These systems interrelate concepts of a particular domain and provide reference to related terms and possible definitions and codes. The purpose of terminology services consists of representing facts that happen in the real world through database management. This process is named Semantic Interoperability. It implies that different systems understand the information they are processing through the use of codes of clinical terminologies. Standard terminologies allow controlling medical vocabulary. But how do we do this? What do we need? Terminology services are a fundamental piece for health data management in health environment
Biomedical term mapping databases
Longer words and phrases are frequently mapped onto a shorter form such as abbreviations or acronyms for efficiency of communication. These abbreviations are pervasive in all aspects of biology and medicine and as the amount of biomedical literature grows, so does the number of abbreviations and the average number of definitions per abbreviation. Even more confusing, different authors will often abbreviate the same word/phrase differently. This ambiguity impedes our ability to retrieve information, integrate databases and mine textual databases for content. Efforts to standardize nomenclature, especially those doing so retrospectively, need to be aware of different abbreviatory mappings and spelling variations. To address this problem, there have been several efforts to develop computer algorithms to identify the mapping of terms between short and long form within a large body of literature. To date, four such algorithms have been applied to create online databases that comprehensively map biomedical terms and abbreviations within MEDLINE: ARGH (http://lethargy.swmed.edu/ARGH/argh.asp), the Stanford Biomedical Abbreviation Server (http://bionlp.stanford.edu/abbreviation/), AcroMed (http://medstract.med.tufts.edu/acro1.1/index.htm) and SaRAD (http://www.hpl.hp.com/research/idl/projects/abbrev.html). In addition to serving as useful computational tools, these databases serve as valuable references that help biologists keep up with an ever-expanding vocabulary of terms
Challenges of connecting chemistry to pharmacology: perspectives from curating the IUPHAR/BPS Guide to PHARMACOLOGY
Connecting chemistry
to pharmacology (c2p) has been an objective of GtoPdb and its precursor
IUPHAR-DB since 2003. This has been achieved by populating our database with
expert-curated relationships between documents, assays, quantitative results,
chemical structures, their locations within the documents and the protein
targets in the assays (D-A-R-C-P). A
wide range of challenges associated with this are described in this perspective,
using illustrative examples from GtoPdb entries. Our selection process begins with judgements
of pharmacological relevance and scientific quality. Even though we have a stringent focus for our
small-data extraction we note that assessing the quality of papers has become
more difficult over the last 15 years. We discuss ambiguity issues with the
resolution of authors’ descriptions of A-R-C-P entities to standardised
identifiers. We also describe developments that have made this somewhat easier
over the same period both in the publication ecosystem as well as enhancements
of our internal processes over recent years.
This perspective concludes with a look at challenges for the future
including the wider capture of mechanistic nuances and possible impacts of text
mining on automated entity extractio
Ontology-Based Clinical Information Extraction Using SNOMED CT
Extracting and encoding clinical information captured in unstructured clinical documents with standard medical terminologies is vital to enable secondary use of clinical data from practice. SNOMED CT is the most comprehensive medical ontology with broad types of concepts and detailed relationships and it has been widely used for many clinical applications. However, few studies have investigated the use of SNOMED CT in clinical information extraction.
In this dissertation research, we developed a fine-grained information model based on the SNOMED CT and built novel information extraction systems to recognize clinical entities and identify their relations, as well as to encode them to SNOMED CT concepts. Our evaluation shows that such ontology-based information extraction systems using SNOMED CT could achieve state-of-the-art performance, indicating its potential in clinical natural language processing
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