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A source of semantically coded Adverse Drug Event (ADE) data can be useful for identifying common phenotypes related to ADEs. We proposed a comprehensive framework for building a standardized ADE knowledge base (called ADEpedia) through combining ontology-based approach with semantic web technology. The framework comprises four primary modules: 1)...
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Context 1
... we downloaded and processed all available SPLs (n=17106, as of 11/18/2010) on the DailyMed. The contents of each label are rendered in a standard XML format, which is used to feed into the XML2RDF transformation module for the RDF transformation. Fig. 2 shows a SPL example in the transformed RDF format. Once a SPL document is transformed into the RDF format, it is loaded into the RDF store by an import script. We transformed and loaded all available SPLs into the RDF store. Secondly, we utilized the RxNav terminology service through an open web service provided by the NLM to retrieve the information about the standard drug ontology RxNorm. In this study, two services were invoked: 1) the service for getting the concept name of a given RxNorm Concept Unique Identifier (RxCUI); 2) the service for getting the structured product label set identifiers for a concept. As each terminology service is usually invoked by a RxCUI, we extracted a full list of RxCUIs (n=194179) from the data table MRCONSO.RRF of a RxNorm version of 10AA_100907F. The results of two services are all rendered in the XML format, which in turn are fed into the XML2RDF transformer. We transformed all those RxCUIs which have at least one mapping to the SPL label and loaded them into the RDF ...
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Citations
... The technological evolution of the tool is also necessary to render it more specific when generating alerts. For example, this could be achieved using the terminology common to all SNOMED CT systems (Systematised Nomenclature of Medicine-Clinical Terms) that determine the active ingredient, the dose, the pharmaceutical form, and the number of packaging units [57][58][59]. To optimise the use of PREFASEG and improve the management of clinical information, intelligent systems such as natural language processing could be applied that would allow the clinician to obtain and interact with the information recorded in text format in the patient's clinical history [60,61]. ...
Background
In 2008, the Institut Català de la Salut (ICS, Catalan Health Institute) implemented a prescription decision support system in its electronic clinical workstation (ECW), which automatically generates online alerts for general practitioners when a possible medication-related problem (MRP) is detected. This tool is known as PREFASEG, and at the time of beginning a new treatment, it automatically assesses the suitability of the treatment for the individual patient. This analysis is based on ongoing treatments, demographic characteristics, existing pathologies, and patient biochemical variables. As a result of the assessment, therapeutic recommendations are provided. The objective of this study is to present the PREFASEG tool, analyse the main alerts that it generates, and determine the degree of alert acceptance.
Methods
A cross-sectional descriptive study was carried out to analyse the generation of MRP-related alerts detected by PREFASEG during 2016, 2017, and 2018 in primary care (PC) in Catalonia. The number of MRP alerts generated, the drugs involved, and the acceptance/rejection of the alerts were analysed. An alert was considered "accepted" when the medication that generated the alert was not prescribed, thereby following the recommendation given by the tool. The MRP alerts studied were therapeutic duplications, safety alerts issued by the Spanish Medicines Agency, and drugs not recommended for use in geriatrics. The prescriptions issued by 6411 ICS PC physicians who use the ECW and provide their services to 5.8 million Catalans through 288 PC teams were analysed.
Results
During the 3 years examined, 67.2 million new prescriptions were analysed, for which PREFASEG generated 4,379,866 alerts (1 for every 15 new treatments). A total of 1,222,159 alerts (28%) were accepted. Pharmacological interactions and therapeutic duplications were the most detected alerts, representing 40 and 30% of the total alerts, respectively. The main pharmacological groups involved in the safety alerts were nonsteroidal anti-inflammatory drugs and renin-angiotensin system inhibitors.
Conclusions
During the period analysed, 28% of the prescriptions wherein a toxicity-related PREFASEG alert was generated led to treatment modification, thereby helping to prevent the generation of potential safety MRPs. However, the tool should be further improved to increase alert acceptance and thereby improve patient safety.
... In the ADEpedia project [14,15], we created a scalable and standardized knowledge base of adverse drug events (ADEs) for pharmacovigilance. In addition, as a part of the ADEpedia project, Wang et al., built a standardized FAERS dataset in a previous study [16]. ...
Objective:
Supplementing the Spontaneous Reporting System (SRS) with Electronic Health Record (EHR) data for adverse drug reaction detection could augment sample size, increase population heterogeneity and cross-validate results for pharmacovigilance research. The difference in the underlying data structures and terminologies between SRS and EHR data presents challenges when attempting to integrate the two into a single database. The Observational Health Data Sciences and Informatics (OHDSI) collaboration provides a Common Data Model (CDM) for organizing and standardizing EHR data to support large-scale observational studies. The objective of the study is to develop and evaluate an informatics platform known as ADEpedia-on-OHDSI, where spontaneous reporting data from FDA's Adverse Event Reporting System (FAERS) is converted into the OHDSI CDM format towards building a next generation pharmacovigilance signal detection platform.
Methods:
An extraction, transformation and loading (ETL) tool was designed, developed, and implemented to convert FAERS data into the OHDSI CDM format. A comprehensive evaluation, including overall ETL evaluation, mapping quality evaluation of drug names to RxNorm, and an evaluation of transformation and imputation quality, was then performed to assess the mapping accuracy and information loss using the FAERS data collected between 2012 and 2017. Previously published findings related to vascular safety profile of triptans were validated using ADEpedia-on-OHDSI in pharmacovigilance research. For the triptan-related vascular event detection, signals were detected by Reporting Odds Ratio (ROR) in high-level group terms (HLGT) level, high-level terms (HLT) level and preferred term (PT) level using the original FAERS data and CDM-based FAERS respectively. In addition, six standardized MedDRA queries (SMQs) related to vascular events were applied.
Results:
A total of 4,619,362 adverse event cases were loaded into 8 tables in the OHDSI CDM. For drug name mapping, 93.9% records and 47.0% unique names were matched with RxNorm codes. Mapping accuracy of drug names was 96% based on a manual verification of randomly sampled 500 unique mappings. Information loss evaluation showed that more than 93% of the data is loaded into the OHDSI CDM for most fields, with the exception of drug route data (66%). The replication study detected 5, 18, 47 and 6, 18, 50 triptan-related vascular event signals in MedDRA HLGT level, HLT level, and PT level for the original FAERS data and CDM-based FAERS respectively. The signal detection scores of six standardized MedDRA queries (SMQs) of vascular events in the raw data study were found to be lower than those scores in the CDM study.
Conclusion:
The outcome of this work would facilitate seamless integration and combined analyses of both SRS and EHR data for pharmacovigilance in ADEpedia-on-OHDSI, our platform for next generation pharmacovigilance.
... Existing frameworks that have integrated various sources in a way that provide some of these features include ADEPedia [13,14], MetaADEDB [15], CATTLE [16], and Bio2RDF [17]. Fig. 1 shows the evidence sources integrated into these systems. ...
Background:
Integrating multiple sources of pharmacovigilance evidence has the potential to advance the science of safety signal detection and evaluation. In this regard, there is a need for more research on how to integrate multiple disparate evidence sources while making the evidence computable from a knowledge representation perspective (i.e., semantic enrichment). Existing frameworks suggest well-promising outcomes for such integration but employ a rather limited number of sources. In particular, none have been specifically designed to support both regulatory and clinical use cases, nor have any been designed to add new resources and use cases through an open architecture. This paper discusses the architecture and functionality of a system called Large-scale Adverse Effects Related to Treatment Evidence Standardization (LAERTES) that aims to address these shortcomings.
Results:
LAERTES provides a standardized, open, and scalable architecture for linking evidence sources relevant to the association of drugs with health outcomes of interest (HOIs). Standard terminologies are used to represent different entities. For example, drugs and HOIs are represented in RxNorm and Systematized Nomenclature of Medicine -- Clinical Terms respectively. At the time of this writing, six evidence sources have been loaded into the LAERTES evidence base and are accessible through prototype evidence exploration user interface and a set of Web application programming interface services. This system operates within a larger software stack provided by the Observational Health Data Sciences and Informatics clinical research framework, including the relational Common Data Model for observational patient data created by the Observational Medical Outcomes Partnership. Elements of the Linked Data paradigm facilitate the systematic and scalable integration of relevant evidence sources.
Conclusions:
The prototype LAERTES system provides useful functionality while creating opportunities for further research. Future work will involve improving the method for normalizing drug and HOI concepts across the integrated sources, aggregated evidence at different levels of a hierarchy of HOI concepts, and developing more advanced user interface for drug-HOI investigations.
... The IHTSDO also works jointly with the WHO and HL7 in order to code their standards with this terminology 60 . In Spain, SNOMED CT is already being used in many normalization projects for clinical information systems 61,62 and can be used to code the medication available, and thus have a linking code between the different medication databases 63 . ...
Clinical Decision Support Systems (CDSS) are computerized tools designed to help healthcare professionals to make clinical and therapeutic decisions, with the objective of improving patient care. Prescription-targeted CDSS have the highest impact in improving patient safety. Although there are different designs and functionalities, all these systems will combine clinical knowledge and patient information in a smart manner, in order to improve the prescription process. With the emergence of new technologies and advances in smart decision systems, the implementation of said systems can achieve an important improvement in terms of the prescription process and patient safety. The design and implementation of these systems should be performed by a multidisciplinary team of professionals, where Pharmacists will play an important role due to their technical knowledge about medications and the technologies associated to their use. This article aims to provide basic guidelines for the design and adequate implementation, monitoring and follow-up of Clinical Decision Support Systems within the setting of pharmacological prescription.
Copyright AULA MEDICA EDICIONES 2014. Published by AULA MEDICA. All rights reserved.
... In our previous and ongoing study, we developed a standardized knowledge base of ADEs known as ADEpedia (http://adepedia.org) leveraging Semantic Web technologies [7]. The ADEpedia is intended to integrate existing known ADE knowledge for drug safety surveillance from disparate resources such as Food and Drug Administration (FDA) Structured Product Labeling (SPL) [7], FDA Adverse Event Reporting System (AERS) [8], and the Unified Medical Language System (UMLS) [9]. ...
... leveraging Semantic Web technologies [7]. The ADEpedia is intended to integrate existing known ADE knowledge for drug safety surveillance from disparate resources such as Food and Drug Administration (FDA) Structured Product Labeling (SPL) [7], FDA Adverse Event Reporting System (AERS) [8], and the Unified Medical Language System (UMLS) [9]. ...
Drug-drug interactions (DDIs) are a major contributing factor for unexpected adverse drug events (ADEs). However, few of knowledge resources cover the severity information of ADEs that is critical for prioritizing the medical need. The objective of the study is to develop and evaluate a Semantic Web-based approach for mining severe DDI-induced ADEs.
We utilized a normalized FDA Adverse Event Report System (AERS) dataset and performed a case study of three frequently prescribed cardiovascular drugs: Warfarin, Clopidogrel and Simvastatin. We extracted putative DDI-ADE pairs and their associated outcome codes. We developed a pipeline to filter the associations using ADE datasets from SIDER and PharmGKB. We also performed a signal enrichment using electronic medical records (EMR) data. We leveraged the Common Terminology Criteria for Adverse Event (CTCAE) grading system and classified the DDI-induced ADEs into the CTCAE in the Web Ontology Language (OWL).
We identified 601 DDI-ADE pairs for the three drugs using the filtering pipeline, of which 61 pairs are in Grade 5, 56 pairs in Grade 4 and 484 pairs in Grade 3. Among 601 pairs, the signals of 59 DDI-ADE pairs were identified from the EMR data.
The approach developed could be generalized to detect the signals of putative severe ADEs induced by DDIs in other drug domains and would be useful for supporting translational and pharmacovigilance study of severe ADEs.
... So far, the FDA has documented PGx information for more than 100 drugs associated with more than 50 genes [1]. Of these drugs, 42 FDA cancer drugs include PGx information in their package inserts. Clearly, cancer therapy is one of the most intensively studied topics in PGx [2][3][4], and relevant PGx data are accumulating quickly. ...
... In our previous study, we have employed NDF-RT and SPLs to annotate drug and disease relationships in the PharmGKB [41]. We will apply the annotation results [41] along with the existing annotations from NDF-RT, ADEpedia [42], LinkedSPLs [43] into the future study, inserting a particular tag for differentiating indications and adverse drug events. ...
Pharmacogenomics (PGx) as an emerging field, is poised to change the way we practice medicine and deliver health care by customizing drug therapies on the basis of each patient's genetic makeup. A large volume of PGx data including information among drugs, genes, and single nucleotide polymorphisms (SNPs) has been accumulated. Normalized and integrated PGx information could facilitate revelation of hidden relationships among drug treatments, genomic variations, and phenotype traits to better support drug discovery and next generation of treatment.
In this study, we generated a normalized and scientific evidence supported cancer based PGx network (CPN) by integrating cancer related PGx information from multiple well-known PGx resources including the Pharmacogenomics Knowledge Base (PharmGKB), the FDA PGx Biomarkers in Drug Labeling, and the Catalog of Published Genome-Wide Association Studies (GWAS). We successfully demonstrated the capability of the CPN for drug repurposing by conducting two case studies.
The CPN established in this study offers comprehensive cancer based PGx information to support cancer orientated research, especially for drug repurposing.
... The ability to predict new drug-target interactions can have applications in drug re-purposing to find new targets for drugs. A related application is drug-drug side effect prediction, e.g., to construct the SIDER [16] side effect resource, or to populate ADEpedia [14], a knowledge base of adverse drug events (ADEs) for drug safety surveillance. ...
The ability to integrate a wealth of human-curated knowledge from scientific datasets and ontologies can benefit drug-target interaction prediction. The hypothesis is that similar drugs interact with the same targets, and similar targets interact with the same drugs. The similarities between drugs reflect a chemical semantic space, while similarities between targets reflect a genomic semantic space. In this paper, we present a novel method that combines a data mining framework for link prediction, semantic knowledge (similarities) from ontologies or semantic spaces, and an algorithmic approach to partition the edges of a heterogeneous graph that includes drug-target interaction edges, and drug-drug and target-target similarity edges. Our semantics based edge partitioning approach, semEP, has the advantages of edge based community detection which allows a node to participate in more than one cluster or community. The semEP problem is to create a minimal partitioning of the edges such that the cluster density of each subset of edges is maximal. We use semantic knowledge (similarities) to specify edge constraints, i.e., specific drug-target interaction edges that should not participate in the same cluster. Using a well-known dataset of drug-target interactions, we demonstrate the benefits of using semEP predictions to improve the performance of a range of state-of-the-art machine learning based prediction methods. Validation of the novel best predicted interactions of semEP against the STITCH interaction resource reflect both accurate and diverse predictions.
... In addition to the significance described in the Introduction section above, the present study was also partially motivated by our previous work on building a standardized knowledge base of ADEs known as ADEpedia, in which we intended to integrate and normalize known ADE knowledge from disparate ADE datasets (e.g., the FDA structured product labels, and the UMLS) [35,36]. We consider that the FDA AERS reporting database is another important data source for ADE knowledge discovery and the normalization of both the drug and ADE names is the first important task we need to tackle with. ...
Background
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.
Results
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.
Conclusions
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.
... Benton et al. [6] conducted a study to identify potential ADEs by using medical message boards and online resources. Jiang et al. [7] developed ADEpedia by using ADE information from SPLs to create a RDF triple store. Each of these studies improve our ability to detect ADEs, which is a crucial step for identifying diseases associated with particular drugs. ...
... The objective of the present study is to build a framework of knowledge integration and discovery that aims to support pharmacogenomics target predication of ADEs. Specifically, we leverage a semantically coded ADE knowledgebase known as ADEpedia [8] and a semantically annotated literature corpus Semantic MEDLINE [9] and integrate them in a semantic web based framework. The ADEpedia (http://adepedia.org), ...
... As mentioned above, the ADEpedia intends to integrate existing known ADE knowledge from disparate resources to achieve a comprehensive ADE knowledgebase [8,13]. In the ADEpedia, the drugs and the ADEs are normalized using the UMLS Concept Unique Identifiers (CUIs). ...
Knowledge-driven text mining is becoming an important research area for identifying pharmacogenomics target genes. However, few of such studies have been focused on the pharmacogenomics targets of adverse drug events (ADEs). The objective of the present study is to build a framework of knowledge integration and discovery that aims to support pharmacogenomics target predication of ADEs. We integrate a semantically annotated literature corpus Semantic MEDLINE with a semantically coded ADE knowledgebase known as ADEpedia using a semantic web based framework. We developed a knowledge discovery approach combining a network analysis of a protein-protein interaction (PPI) network and a gene functional classification approach. We performed a case study of drug-induced long QT syndrome for demonstrating the usefulness of the framework in predicting potential pharmacogenomics targets of ADEs.
