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Olivier Bodenreider - One of the best experts on this subject based on the ideXlab platform.

  • MedInfo - Normalizing Dietary Supplement Product Names Using the RxNorm Model.
    Studies in health technology and informatics, 2019
    Co-Authors: Jake Vasilakes, Olivier Bodenreider, Yadan Fan, Rubina F Rizvi, Anusha Bompelli, Rui Zhang
    Abstract:

    The use of dietary supplements (DSs) is increasing in the U.S. As such, it is crucial for consumers, clinicians, and researchers to be able to find information about DS products. However, labeling regulations allow great variability in DS product names, which makes searching for this information difficult. Following the RxNorm drug name normalization model, we developed a rule-based natural language processing system to normalize DS product names using pattern templates. We evaluated the system on product names extracted from the Dietary Supplement Label Database. Our system generated 136 unique templates and obtained a coverage of 72%, a 32% increase over the existing RxNorm model. Manual review showed that our system achieved a normalization accuracy of 0.86. We found that the normalization of DS product names is feasible, but more work is required to improve the generalizability of the system.

  • normalizing dietary supplement product names using the RxNorm model
    World Congress on Medical and Health Informatics Medinfo, 2019
    Co-Authors: Jake Vasilakes, Olivier Bodenreider, Yadan Fan, Rubina F Rizvi, Anusha Bompelli, Rui Zhang
    Abstract:

    The use of dietary supplements (DSs) is increasing in the U.S. As such, it is crucial for consumers, clinicians, and researchers to be able to find information about DS products. However, labeling regulations allow great variability in DS product names, which makes searching for this information difficult. Following the RxNorm drug name normalization model, we developed a rule-based natural language processing system to normalize DS product names using pattern templates. We evaluated the system on product names extracted from the Dietary Supplement Label Database. Our system generated 136 unique templates and obtained a coverage of 72%, a 32% increase over the existing RxNorm model. Manual review showed that our system achieved a normalization accuracy of 0.86. We found that the normalization of DS product names is feasible, but more work is required to improve the generalizability of the system.

  • terminology status apis mapping obsolete codes to current RxNorm snomed ct and loinc concepts
    Studies in health technology and informatics, 2017
    Co-Authors: Lee B. Peters, Thang Nguyen, Olivier Bodenreider
    Abstract:

    We created the Terminology Status Application Programming Interface (API) to assist users in mapping obsolete codes to current RxNorm, SNOMED CT and LOINC concepts. Use cases include support for information retrieval, maintenance of value sets, and analytics of legacy clinical databases. Our terminology status APIs typically receive over 4 million calls per month on average.

  • MedInfo - Terminology Status APIs - Mapping Obsolete Codes to Current RxNorm, SNOMED CT, and LOINC Concepts.
    Studies in health technology and informatics, 2017
    Co-Authors: Lee B. Peters, Thang Nguyen, Olivier Bodenreider
    Abstract:

    We created the Terminology Status Application Programming Interface (API) to assist users in mapping obsolete codes to current RxNorm, SNOMED CT and LOINC concepts. Use cases include support for information retrieval, maintenance of value sets, and analytics of legacy clinical databases. Our terminology status APIs typically receive over 4 million calls per month on average.

  • AMIA - Approaches to Supporting the Analysis of Historical Medication Datasets with RxNorm.
    AMIA ... Annual Symposium proceedings. AMIA Symposium, 2015
    Co-Authors: Lee B. Peters, Olivier Bodenreider
    Abstract:

    Objective: To investigate approaches to supporting the analysis of historical medication datasets with RxNorm. Methods: We created two sets of National Drug Codes (NDCs). One is based on historical NDCs harvested from versions of RxNorm from 2007 to present . The other comprises all sources of NDCs in the current release of RxNorm, including proprietary sources. We evaluated these two resources against four sets of NDCs obtained from various sources. Results: In two historical medication datasets, 14-19% of the NDCs were obsolete, but 91-96% of these obsolete NDCs could be recovered and mapped to active drug concepts. Conclusion: Adding historical data significantly increases NDC mapping to active RxNorm drugs. A service for mapping historical NDC datasets leveraging RxNorm was added to the RxNorm API and is available at https://rxnav.nlm.nih.gov/.

Christopher G. Chute - One of the best experts on this subject based on the ideXlab platform.

  • Analyzing categorical information in two publicly available drug terminologies
    2016
    Co-Authors: Jyotishman Pathak, Christopher G. Chute
    Abstract:

    Background The RxNorm and NDF-RT (National Drug File Reference Terminology) are a suite of terminology standards for clinical drugs designated for use in the US federal government systems for electronic exchange of clinical health information. Analyzing how different drug products described in these terminologies are categorized into drug classes will help in their better organization and classification of pharmaceutical information. Methods Mappings between drug products in RxNorm and NDF-RT drug classes were extracted. Mappings were also extracted between drug products in RxNorm to five high-level NDF-RT categories: Chemical Structure; cellular or subcellular Mechanism of Action; organ-level or system-level Physiologic Effect; Therapeutic Intent; and Pharmacokinetics. Coverage for the mappings and the gaps were evaluated and analyzed algorithmically. Results Approximately 54 % of RxNorm drug products (Semantic Clinical Drugs) were found not to have a correspondence in NDF-RT. Similarly, approximately 45 % of drug products in NDF-RT are missing from RxNorm, most of which can be attributed to differences in dosage, strength, and route form. Approximately 81

  • AMIA - Evaluation of RxNorm for Medication Clinical Decision Support
    AMIA ... Annual Symposium proceedings. AMIA Symposium, 2014
    Co-Authors: Robert R. Freimuth, Qian Zhu, Kelly Wix, Mark H. Siska, Christopher G. Chute
    Abstract:

    We evaluated the potential use of RxNorm to provide standardized representations of generic drug name and route of administration to facilitate management of drug lists for clinical decision support (CDS) rules. We found a clear representation of generic drug name but not route of administration. We identified several issues related to data quality, including erroneous or missing defined relationships, and the use of different concept hierarchies to represent the same drug. More importantly, we found extensive semantic precoordination of orthogonal concepts related to route and dose form, which would complicate the use of RxNorm for drug-based CDS. This study demonstrated that while RxNorm is a valuable resource for the standardization of medications used in clinical practice, additional work is required to enhance the terminology so that it can support expanded use cases, such as managing drug lists for CDS.

  • evaluation of RxNorm for medication clinical decision support
    American Medical Informatics Association Annual Symposium, 2014
    Co-Authors: Robert R. Freimuth, Qian Zhu, Kelly Wix, Mark H. Siska, Christopher G. Chute
    Abstract:

    We evaluated the potential use of RxNorm to provide standardized representations of generic drug name and route of administration to facilitate management of drug lists for clinical decision support (CDS) rules. We found a clear representation of generic drug name but not route of administration. We identified several issues related to data quality, including erroneous or missing defined relationships, and the use of different concept hierarchies to represent the same drug. More importantly, we found extensive semantic precoordination of orthogonal concepts related to route and dose form, which would complicate the use of RxNorm for drug-based CDS. This study demonstrated that while RxNorm is a valuable resource for the standardization of medications used in clinical practice, additional work is required to enhance the terminology so that it can support expanded use cases, such as managing drug lists for CDS.

  • MedXN: an open source medication extraction and normalization tool for clinical text.
    Journal of the American Medical Informatics Association : JAMIA, 2014
    Co-Authors: Sunghwan Sohn, Christopher G. Chute, Sean P. Murphy, Cheryl Clark, Scott Halgrim, Hongfang Liu
    Abstract:

    Objective We developed the Medication Extraction and Normalization (MedXN) system to extract comprehensive medication information and normalize it to the most appropriate RxNorm concept unique identifier (RxCUI) as specifically as possible. Methods Medication descriptions in clinical notes were decomposed into medication name and attributes, which were separately extracted using RxNorm dictionary lookup and regular expression. Then, each medication name and its attributes were combined together according to RxNorm convention to find the most appropriate RxNorm representation. To do this, we employed serialized hierarchical steps implemented in Apache's Unstructured Information Management Architecture. We also performed synonym expansion, removed false medications, and employed inference rules to improve the medication extraction and normalization performance. Results An evaluation on test data of 397 medication mentions showed F-measures of 0.975 for medication name and over 0.90 for most attributes. The RxCUI assignment produced F-measures of 0.932 for medication name and 0.864 for full medication information. Most false negative RxCUI assignments in full medication information are due to human assumption of missing attributes and medication names in the gold standard. Conclusions The MedXN system ( ) was able to extract comprehensive medication information with high accuracy and demonstrated good normalization capability to RxCUI as long as explicit evidence existed. More sophisticated inference rules might result in further improvements to specific RxCUI assignments for incomplete medication descriptions.

  • Open Access Profiling structured product labeling with NDF-RT
    2014
    Co-Authors: Qian Zhu, Guoqian Jiang, Christopher G. Chute
    Abstract:

    Background: Structured Product Labeling (SPL) is a document markup standard approved by Health Level Seven (HL7) and adopted by United States Food and Drug Administration (FDA) as a mechanism for exchanging drug product information. The SPL drug labels contain rich information about FDA approved clinical drugs. However, the lack of linkage to standard drug ontologies hinders their meaningful use. NDF-RT (National Drug File Reference Terminology) and NLM RxNorm as standard drug ontology were used to standardize and profile the product labels. Methods: In this paper, we present a framework that intends to map SPL drug labels with existing drug ontologies: NDF-RT and RxNorm. We also applied existing categorical annotations from the drug ontologies to classify SPL drug labels into corresponding classes. We established the classification and relevant linkage for SPL drug labels using the following three approaches. First, we retrieved NDF-RT categorical information from the External Pharmacologic Class (EPC) indexing SPLs. Second, we used the RxNorm and NDF-RT mappings to classify and link SPLs with NDF-RT categories. Third, we profiled SPLs using RxNorm term type information. In the implementation process, we employed a Semantic Web technology framework, in which we stored the data sets from NDF-RT and SPLs into a RDF triple store, and executed SPARQL queries to retrieve data from customized SPARQL endpoints. Meanwhile, we imported RxNorm data into MySQL relational database. Results: In total, 96.0 % SPL drug labels were mapped with NDF-RT categories whereas 97.0 % SPL drug labels are linked to RxNorm codes. We found that the majority of SPL drug labels are mapped to chemical ingredient concepts in both drug ontologies whereas a relatively small portion of SPL drug labels are mapped to clinical drug concepts. Conclusions: The profiling outcomes produced by this study would provide useful insights on meaningful use of FDA SPL drug labels in clinical applications through standard drug ontologies such as NDF-RT and RxNorm

Lee B. Peters - One of the best experts on this subject based on the ideXlab platform.

  • terminology status apis mapping obsolete codes to current RxNorm snomed ct and loinc concepts
    Studies in health technology and informatics, 2017
    Co-Authors: Lee B. Peters, Thang Nguyen, Olivier Bodenreider
    Abstract:

    We created the Terminology Status Application Programming Interface (API) to assist users in mapping obsolete codes to current RxNorm, SNOMED CT and LOINC concepts. Use cases include support for information retrieval, maintenance of value sets, and analytics of legacy clinical databases. Our terminology status APIs typically receive over 4 million calls per month on average.

  • MedInfo - Terminology Status APIs - Mapping Obsolete Codes to Current RxNorm, SNOMED CT, and LOINC Concepts.
    Studies in health technology and informatics, 2017
    Co-Authors: Lee B. Peters, Thang Nguyen, Olivier Bodenreider
    Abstract:

    We created the Terminology Status Application Programming Interface (API) to assist users in mapping obsolete codes to current RxNorm, SNOMED CT and LOINC concepts. Use cases include support for information retrieval, maintenance of value sets, and analytics of legacy clinical databases. Our terminology status APIs typically receive over 4 million calls per month on average.

  • AMIA - Approaches to Supporting the Analysis of Historical Medication Datasets with RxNorm.
    AMIA ... Annual Symposium proceedings. AMIA Symposium, 2015
    Co-Authors: Lee B. Peters, Olivier Bodenreider
    Abstract:

    Objective: To investigate approaches to supporting the analysis of historical medication datasets with RxNorm. Methods: We created two sets of National Drug Codes (NDCs). One is based on historical NDCs harvested from versions of RxNorm from 2007 to present . The other comprises all sources of NDCs in the current release of RxNorm, including proprietary sources. We evaluated these two resources against four sets of NDCs obtained from various sources. Results: In two historical medication datasets, 14-19% of the NDCs were obsolete, but 91-96% of these obsolete NDCs could be recovered and mapped to active drug concepts. Conclusion: Adding historical data significantly increases NDC mapping to active RxNorm drugs. A service for mapping historical NDC datasets leveraging RxNorm was added to the RxNorm API and is available at https://rxnav.nlm.nih.gov/.

  • approaches to supporting the analysis of historical medication datasets with RxNorm
    American Medical Informatics Association Annual Symposium, 2015
    Co-Authors: Lee B. Peters, Olivier Bodenreider
    Abstract:

    Objective: To investigate approaches to supporting the analysis of historical medication datasets with RxNorm. Methods: We created two sets of National Drug Codes (NDCs). One is based on historical NDCs harvested from versions of RxNorm from 2007 to present . The other comprises all sources of NDCs in the current release of RxNorm, including proprietary sources. We evaluated these two resources against four sets of NDCs obtained from various sources. Results: In two historical medication datasets, 14-19% of the NDCs were obsolete, but 91-96% of these obsolete NDCs could be recovered and mapped to active drug concepts. Conclusion: Adding historical data significantly increases NDC mapping to active RxNorm drugs. A service for mapping historical NDC datasets leveraging RxNorm was added to the RxNorm API and is available at https://rxnav.nlm.nih.gov/.

  • AMIA - RxClass - Navigating between Drug Classes and RxNorm Drugs.
    2014
    Co-Authors: Thang Nguyen, Lee B. Peters, Olivier Bodenreider
    Abstract:

    Objectives: To demonstrate RxClass, a web interactive browser to explore the relationships between RxNorm drugs and drug classes from several sources including ATC, MeSH and NDF-RT. RxClass is publicly available at: http://mor.nlm.nih.gov/RxClass/ I. MOTIVATION Drug classes constitute important information about the drugs and are critical to important use cases, such as clinical decision support (e.g., for allergy checking). RxNav, our RxNorm browser, already displays the classes for RxNorm drugs, but its drug-centric perspective does not accommodate the exploration of drug classes. This is the reason why we developed a web-based companion browser, RxClass, which supports navigation between RxNorm drugs and drug classes from several sources, including ATC, MeSH, NDF-RT and Structured Product Labels from the Food and Drug Administration (FDA). II. SOURCES OF CLASS TYPES AND DRUG-CLASS RELATIONS The Anatomical Therapeutic Chemical drug classification (ATC) is a resource developed for pharmacoepidemiology purposes by the World Health Organization Collaborating Centre for Drug Statistics Methodology. The Medical Subject Headings (MeSH), developed by the National Library of Medicine (NLM), provides a rich description of pharmacological actions for the purpose of indexing and retrieval of biomedical articles. The National Drug File-Reference Terminology (NDF-RT), developed by the Department of Veterans Affairs, provides clinical information about drugs and contains FDA Established Pharmacologic Classification (EPC), Disease classification, Chemical Structure and Classification (Chem), Mechanism of Action (MOA), Physiologic Effects (PE) and Pharmacokinetics (PK) class types. ATC and MeSH provide both the vocabulary for drug classes and the drug-class membership relations. In contrast, as shown in Table 1, several sources (DailyMed, FDASPL and NDF-RT) provide drug-class membership relations in reference to the NDF-RT vocabulary for classes. All drugs are normalized to RxNorm. III. RXCLASS INTERFACE Like RxNav, RxClass is supported by functions from an application programming interface (API), which can be used independently for integrating drug class information in programs. The API serves the latest information available from the drug information sources. RxClass provides a graphical interface to explore the hierarchical class structures of each source and examine the corresponding RxNorm drug members for each class. Some features of RxClass: • The user can navigate through the drug classes via the hierarchical menu, or use the search feature to identify a drug class or RxNorm drug (Figure 1). • RxClass supports the exploration of all classes for a given drug across multiple classifications (Figure 2). • RxClass contains an autocomplete function which will help identify class or drug names in search mode, as well as spelling suggestions for misspelled drug and class names during search. TABLE I. CLASS TYPE MAPPING BY DRUG SOURCES Class Type Source of Drug-Class Relations ATC MeSH DailyMed FDASPL NDF-RT ATC X

John Kilbourne - One of the best experts on this subject based on the ideXlab platform.

  • Evaluating the implementation of RxNorm in ambulatory electronic prescriptions.
    Journal of the American Medical Informatics Association, 2015
    Co-Authors: Ajit A. Dhavle, John Kilbourne, Stacy Ward-charlerie, Michael T. Rupp, Vishal P. Amin, Joshua Ruiz
    Abstract:

    Objective RxNorm is a standardized drug nomenclature maintained by the National Library of Medicine that has been recommended as an alternative to the National Drug Code (NDC) terminology for use in electronic prescribing. The objective of this study was to evaluate the implementation of RxNorm in ambulatory care electronic prescriptions (e-prescriptions). Methods We analyzed a random sample of 49 997 e-prescriptions that were received by 7391 locations of a national retail pharmacy chain during a single day in April 2014. The e-prescriptions in the sample were generated by 37 801 ambulatory care prescribers using 519 different e-prescribing software applications. Results We found that 97.9% of e-prescriptions in the study sample could be accurately represented by an RxNorm identifier. However, RxNorm identifiers were actually used as drug identifiers in only 16 433 (33.0%) e-prescriptions. Another 431 (2.5%) e-prescriptions that used RxNorm identifiers had a discrepancy in the corresponding Drug Database Code qualifier field or did not have a qualifier (Term Type) at all. In 10 e-prescriptions (0.06%), the free-text drug description and the RxNorm concept unique identifier pointed to completely different drug concepts, and in 7 e-prescriptions (0.04%), the NDC and RxNorm drug identifiers pointed to completely different drug concepts. Discussion The National Library of Medicine continues to enhance the RxNorm terminology and expand its scope. This study illustrates the need for technology vendors to improve their implementation of RxNorm; doing so will accelerate the adoption of RxNorm as the preferred alternative to using the NDC terminology in e-prescribing.

  • Evaluating the implementation of RxNorm in ambulatory electronic prescriptions.
    Journal of the American Medical Informatics Association : JAMIA, 2015
    Co-Authors: Ajit A. Dhavle, John Kilbourne, Stacy Ward-charlerie, Michael T. Rupp, Vishal P. Amin, Joshua Ruiz
    Abstract:

    RxNorm is a standardized drug nomenclature maintained by the National Library of Medicine that has been recommended as an alternative to the National Drug Code (NDC) terminology for use in electronic prescribing. The objective of this study was to evaluate the implementation of RxNorm in ambulatory care electronic prescriptions (e-prescriptions). We analyzed a random sample of 49 997 e-prescriptions that were received by 7391 locations of a national retail pharmacy chain during a single day in April 2014. The e-prescriptions in the sample were generated by 37 801 ambulatory care prescribers using 519 different e-prescribing software applications. We found that 97.9% of e-prescriptions in the study sample could be accurately represented by an RxNorm identifier. However, RxNorm identifiers were actually used as drug identifiers in only 16 433 (33.0%) e-prescriptions. Another 431 (2.5%) e-prescriptions that used RxNorm identifiers had a discrepancy in the corresponding Drug Database Code qualifier field or did not have a qualifier (Term Type) at all. In 10 e-prescriptions (0.06%), the free-text drug description and the RxNorm concept unique identifier pointed to completely different drug concepts, and in 7 e-prescriptions (0.04%), the NDC and RxNorm drug identifiers pointed to completely different drug concepts. The National Library of Medicine continues to enhance the RxNorm terminology and expand its scope. This study illustrates the need for technology vendors to improve their implementation of RxNorm; doing so will accelerate the adoption of RxNorm as the preferred alternative to using the NDC terminology in e-prescribing. Published by Oxford University Press on behalf of the American Medical Informatics Association 2015. This work is written by US Government employees and is in the public domain in the US.

  • main.html
    2015
    Co-Authors: Kelly Zeng, John Kilbourne, The Original Rxnav
    Abstract:

    essary when additional information sources are inte-grated. The default view in the original RxNav, displaying the relations between drug names from RxNorm, has be-come the terminology tab. Other views under devel-opment include the clinical tab and the label tab

  • Normalized names for clinical drugs: RxNorm at 6 years
    Journal of the American Medical Informatics Association : JAMIA, 2011
    Co-Authors: Stuart J. Nelson, Kelly Zeng, John Kilbourne, Tammy Powell, Robin Moore
    Abstract:

    Objective In the 6 years since the National Library of Medicine began monthly releases of RxNorm, RxNorm has become a central resource for communicating about clinical drugs and supporting interoperation between drug vocabularies. Materials and methods Built on the idea of a normalized name for a medication at a given level of abstraction, RxNorm provides a set of names and relationships based on 11 different external source vocabularies. The standard model enables decision support to take place for a variety of uses at the appropriate level of abstraction. With the incorporation of National Drug File Reference Terminology (NDF-RT) from the Veterans Administration, even more sophisticated decision support has become possible. Discussion While related products such as RxTerms, RxNav, MyMedicationList, and MyRxPad have been recognized as helpful for various uses, tasks such as identifying exactly what is and is not on the market remain a challenge.

  • Building a standards-based and collaborative e-prescribing tool: MyRxPad
    International journal of data mining and bioinformatics, 2011
    Co-Authors: Stuart J. Nelson, Kelly Zeng, John Kilbourne
    Abstract:

    MyRxPad (rxp.nlm.nih.gov) is a prototype application intended to enable a practitioner patient collaborative approach towards e-prescribing: patients play an active role by maintaining up-to-date and accurate medication lists. Prescribers make well-informed and safe prescribing decisions based on personal medication records contributed by patients. MyRxPad is thus the vehicle for collaborations with patients using MyMedicationList (MML). Integration with personal medication records in the context of e-prescribing is thus enabled. We present our experience in applying RxNorm in an e-prescribing setting: using standard names and codes to capture prescribed medication as well as extracting information from RxNorm to support medication-related clinical decision.

Guoqian Jiang - One of the best experts on this subject based on the ideXlab platform.

  • toward a normalized clinical drug knowledge base in china applying the RxNorm model to chinese clinical drugs
    Journal of the American Medical Informatics Association, 2018
    Co-Authors: Guoqian Jiang, Yaoyun Zhang, Min Jiang, Li Wang, Jingqi Wang, Jiancheng Dong, Yun Liu, Cui Tao, Yi Zhou
    Abstract:

    Objective In recent years, electronic health record systems have been widely implemented in China, making clinical data available electronically. However, little effort has been devoted to making drug information exchangeable among these systems. This study aimed to build a Normalized Chinese Clinical Drug (NCCD) knowledge base, by applying and extending the information model of RxNorm to Chinese clinical drugs. Methods Chinese drugs were collected from 4 major resources-China Food and Drug Administration, China Health Insurance Systems, Hospital Pharmacy Systems, and China Pharmacopoeia-for integration and normalization in NCCD. Chemical drugs were normalized using the information model in RxNorm without much change. Chinese patent drugs (i.e., Chinese herbal extracts), however, were represented using an expanded RxNorm model to incorporate the unique characteristics of these drugs. A hybrid approach combining automated natural language processing technologies and manual review by domain experts was then applied to drug attribute extraction, normalization, and further generation of drug names at different specification levels. Lastly, we reported the statistics of NCCD, as well as the evaluation results using several sets of randomly selected Chinese drugs. Results The current version of NCCD contains 16 976 chemical drugs and 2663 Chinese patent medicines, resulting in 19 639 clinical drugs, 250 267 unique concepts, and 2 602 760 relations. By manual review of 1700 chemical drugs and 250 Chinese patent drugs randomly selected from NCCD (about 10%), we showed that the hybrid approach could achieve an accuracy of 98.60% for drug name extraction and normalization. Using a collection of 500 chemical drugs and 500 Chinese patent drugs from other resources, we showed that NCCD achieved coverages of 97.0% and 90.0% for chemical drugs and Chinese patent drugs, respectively. Conclusion Evaluation results demonstrated the potential to improve interoperability across various electronic drug systems in China.

  • Standardizing adverse drug event reporting data
    Journal of biomedical semantics, 2014
    Co-Authors: Liwei Wang, Guoqian Jiang, Hongfang Liu
    Abstract:

    The Adverse Event Reporting System (AERS) is an FDA database providing rich information on voluntary reports of adverse drug events (ADEs). Normalizing data in the AERS would improve the mining capacity of the AERS for drug safety signal detection and promote semantic interoperability between the AERS and other data sources. In this study, we normalize the AERS and build a publicly available normalized ADE data source. The drug information in the AERS is normalized to RxNorm, a standard terminology source for medication, using a natural language processing medication extraction tool, MedEx. Drug class information is then obtained from the National Drug File-Reference Terminology (NDF-RT) using a greedy algorithm. Adverse events are aggregated through mapping with the Preferred Term (PT) and System Organ Class (SOC) codes of Medical Dictionary for Regulatory Activities (MedDRA). The performance of MedEx-based annotation was evaluated and case studies were performed to demonstrate the usefulness of our approaches. Our study yields an aggregated knowledge-enhanced AERS data mining set (AERS-DM). In total, the AERS-DM contains 37,029,228 Drug-ADE records. Seventy-one percent (10,221/14,490) of normalized drug concepts in the AERS were classified to 9 classes in NDF-RT. The number of unique pairs is 4,639,613 between RxNorm concepts and MedDRA Preferred Term (PT) codes and 205,725 between RxNorm concepts and SOC codes after ADE aggregation. We have built an open-source Drug-ADE knowledge resource with data being normalized and aggregated using standard biomedical ontologies. The data resource has the potential to assist the mining of ADE from AERS for the data mining research community.

  • Open Access Profiling structured product labeling with NDF-RT
    2014
    Co-Authors: Qian Zhu, Guoqian Jiang, Christopher G. Chute
    Abstract:

    Background: Structured Product Labeling (SPL) is a document markup standard approved by Health Level Seven (HL7) and adopted by United States Food and Drug Administration (FDA) as a mechanism for exchanging drug product information. The SPL drug labels contain rich information about FDA approved clinical drugs. However, the lack of linkage to standard drug ontologies hinders their meaningful use. NDF-RT (National Drug File Reference Terminology) and NLM RxNorm as standard drug ontology were used to standardize and profile the product labels. Methods: In this paper, we present a framework that intends to map SPL drug labels with existing drug ontologies: NDF-RT and RxNorm. We also applied existing categorical annotations from the drug ontologies to classify SPL drug labels into corresponding classes. We established the classification and relevant linkage for SPL drug labels using the following three approaches. First, we retrieved NDF-RT categorical information from the External Pharmacologic Class (EPC) indexing SPLs. Second, we used the RxNorm and NDF-RT mappings to classify and link SPLs with NDF-RT categories. Third, we profiled SPLs using RxNorm term type information. In the implementation process, we employed a Semantic Web technology framework, in which we stored the data sets from NDF-RT and SPLs into a RDF triple store, and executed SPARQL queries to retrieve data from customized SPARQL endpoints. Meanwhile, we imported RxNorm data into MySQL relational database. Results: In total, 96.0 % SPL drug labels were mapped with NDF-RT categories whereas 97.0 % SPL drug labels are linked to RxNorm codes. We found that the majority of SPL drug labels are mapped to chemical ingredient concepts in both drug ontologies whereas a relatively small portion of SPL drug labels are mapped to clinical drug concepts. Conclusions: The profiling outcomes produced by this study would provide useful insights on meaningful use of FDA SPL drug labels in clinical applications through standard drug ontologies such as NDF-RT and RxNorm

  • profiling structured product labeling with ndf rt and RxNorm
    Journal of Biomedical Semantics, 2012
    Co-Authors: Guoqian Jiang, Christopher G. Chute
    Abstract:

    Background Structured Product Labeling (SPL) is a document markup standard approved by Health Level Seven (HL7) and adopted by United States Food and Drug Administration (FDA) as a mechanism for exchanging drug product information. The SPL drug labels contain rich information about FDA approved clinical drugs. However, the lack of linkage to standard drug ontologies hinders their meaningful use. NDF-RT (National Drug File Reference Terminology) and NLM RxNorm as standard drug ontology were used to standardize and profile the product labels.

  • Profiling structured product labeling with NDF-RT and RxNorm
    Journal of Biomedical Semantics, 2012
    Co-Authors: Qian Zhu, Guoqian Jiang, Christopher G. Chute
    Abstract:

    Background Structured Product Labeling (SPL) is a document markup standard approved by Health Level Seven (HL7) and adopted by United States Food and Drug Administration (FDA) as a mechanism for exchanging drug product information. The SPL drug labels contain rich information about FDA approved clinical drugs. However, the lack of linkage to standard drug ontologies hinders their meaningful use. NDF-RT (National Drug File Reference Terminology) and NLM RxNorm as standard drug ontology were used to standardize and profile the product labels. Methods In this paper, we present a framework that intends to map SPL drug labels with existing drug ontologies: NDF-RT and RxNorm. We also applied existing categorical annotations from the drug ontologies to classify SPL drug labels into corresponding classes. We established the classification and relevant linkage for SPL drug labels using the following three approaches. First, we retrieved NDF-RT categorical information from the External Pharmacologic Class (EPC) indexing SPLs. Second, we used the RxNorm and NDF-RT mappings to classify and link SPLs with NDF-RT categories. Third, we profiled SPLs using RxNorm term type information. In the implementation process, we employed a Semantic Web technology framework, in which we stored the data sets from NDF-RT and SPLs into a RDF triple store, and executed SPARQL queries to retrieve data from customized SPARQL endpoints. Meanwhile, we imported RxNorm data into MySQL relational database. Results In total, 96.0% SPL drug labels were mapped with NDF-RT categories whereas 97.0% SPL drug labels are linked to RxNorm codes. We found that the majority of SPL drug labels are mapped to chemical ingredient concepts in both drug ontologies whereas a relatively small portion of SPL drug labels are mapped to clinical drug concepts. Conclusions The profiling outcomes produced by this study would provide useful insights on meaningful use of FDA SPL drug labels in clinical applications through standard drug ontologies such as NDF-RT and RxNorm.