The SADI Personal Health Lens: A Web Browser-Based System for Identifying Personally Relevant Drug Interactions

Vandervalk, Ben; McCarthy, Erin; Cruz-Toledo, José; Klein, Artjom; Baker, Christopher J. O.; Dumontier, Michel; Wilkinson, Mark D.

doi:10.2196/resprot.2315

Cited by 10 publications

(14 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Text mining researchers might also find the PDDI synthesis useful for identifying gaps in PDDI information sources that text mining might be able to address. The development of a common PDDI framework could also benefit United States healthcare organizations who are currently striving to incorporate PDDI screening along with other strategies to achieve meaningful use of electronic medical records [9], [10]; drug-safety scientists who monitor post-market data related to drug use for new concerns [11]; researchers in drug development who build in silico models to help identify new drug candidates or drugs that can be ‘repositioned’ for new uses [12]; those who create and maintain drug information resources that help clinicians guide patients to safe and effective medication therapies [1]; and patients seeking information on the safety of the medicines they take [13]. …”

Section: Introductionmentioning

confidence: 99%

Toward a complete dataset of drug–drug interaction information from publicly available sources

Ayvaz

Horn

Hassanzadeh

et al. 2015

Journal of Biomedical Informatics

Self Cite

105

View full text Add to dashboard Cite

Although potential drug-drug interactions (PDDIs) are a significant source of preventable drug-related harm, there is currently no single complete source of PDDI information. In the current study, all publically available sources of PDDI information that could be identified using a comprehensive and broad search were combined into a single dataset. The combined dataset merged fourteen different sources including 5 clinically-oriented information sources, 4 Natural Language Processing (NLP) Corpora, and 5 Bioinformatics/Pharmacovigilance information sources. As a comprehensive PDDI source, the merged dataset might benefit the pharmacovigilance text mining community by making it possible to compare the representativeness of NLP corpora for PDDI text extraction tasks, and specifying elements that can be useful for future PDDI extraction purposes. An analysis of the overlap between and across the data sources showed that there was little overlap. Even comprehensive PDDI lists such as DrugBank, KEGG, and the NDF-RT had less than 50% overlap with each other. Moreover, all of the comprehensive lists had incomplete coverage of two data sources that focus on PDDIs of interest in most clinical settings. Based on this information, we think that systems that provide access to the comprehensive lists, such as APIs into RxNorm, should be careful to inform users that the lists may be incomplete with respect to PDDIs that drug experts suggest clinicians be aware of. In spite of the low degree of overlap, several dozen cases were identified where PDDI information provided in drug product labeling might be augmented by the merged dataset. Moreover, the combined dataset was also shown to improve the performance of an existing PDDI NLP pipeline and a recently published PDDI pharmacovigilance protocol. Future work will focus on improvement of the methods for mapping between PDDI information sources, identifying methods to improve the use of the merged dataset in PDDI NLP algorithms, integrating high-quality PDDI information from the merged dataset into Wikidata, and making the combined dataset accessible as Semantic Web Linked Data.

show abstract

Section: Introductionmentioning

confidence: 99%

Toward a complete dataset of drug–drug interaction information from publicly available sources

Ayvaz

Horn

Hassanzadeh

et al. 2015

Journal of Biomedical Informatics

Self Cite

105

View full text Add to dashboard Cite

show abstract

“…These are axioms of similar syntax [18]. All the regularities that can exist in an ontology do not necessarily highlight a corresponding ‘design’ pattern.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper we describe a refinement of the framework from [18] and we demonstrate its usage with three modules [21] from SNOMED-CT. We wanted to detect and further analyse the regularities and irregularities in the modules of the ontology, and find how these can be linked to potential design defects in the ontology that have been reported in past work. The assumption made for these defects is that entities that follow naming conventions should also follow a similar pattern in the description of their usage axioms.…”

Section: Introductionmentioning

confidence: 99%

Analysing Syntactic Regularities and Irregularities in SNOMED-CT

et al. 2012

View full text Add to dashboard Cite

MotivationIn this paper we demonstrate the usage of RIO; a framework for detecting syntactic regularities using cluster analysis of the entities in the signature of an ontology. Quality assurance in ontologies is vital for their use in real applications, as well as a complex and difficult task. It is also important to have such methods and tools when the ontology lacks documentation and the user cannot consult the ontology developers to understand its construction. One aspect of quality assurance is checking how well an ontology complies with established ‘coding standards’; is the ontology regular in how descriptions of different types of entities are axiomatised? Is there a similar way to describe them and are there any corner cases that are not covered by a pattern? Detection of regularities and irregularities in axiom patterns should provide ontology authors and quality inspectors with a level of abstraction such that compliance to coding standards can be automated. However, there is a lack of such reverse ontology engineering methods and tools.ResultsRIO framework allows regularities to be detected in an OWL ontology, i.e. repetitive structures in the axioms of an ontology. We describe the use of standard machine learning approaches to make clusters of similar entities and generalise over their axioms to find regularities. This abstraction allows matches to, and deviations from, an ontology’s patterns to be shown. We demonstrate its usage with the inspection of three modules from SNOMED-CT, a large medical terminology, that cover “Present” and “Absent” findings, as well as “Chronic” and “Acute” findings. The module sizes are 5 065, 20 688 and 19 812 asserted axioms. They are analysed in terms of their types and number of regularities and irregularities in the asserted axioms of the ontology. The analysis showed that some modules of the terminology, which were expected to instantiate a pattern described in the SNOMED-CT technical guide, were found to have a high number of regularity deviations. A subset of these were categorised as “design defects” by verifying them with past work on the quality assurance of SNOMED-CT. These were mainly incomplete descriptions. In the worst case, the expected patterns described in the technical guide were followed by only 5% of the axioms in the module.ConclusionIt is possible to automatically detect regularities and then inspect irregularities in an ontology. We argue that RIO is a tool to find and report such matches and mismatches, for evaluations by the domain experts. We have demonstrated that standard clustering techniques from machine learning can offer a tool in the drive for quality assurance in ontologies.Availabilityhttp://riotool.sourceforge.net/Contacthttp://eleni.mikroyannidi@manchester.ac.uk, http://robert.stevens@manchehster.ac.uk

show abstract

“…HWS is already preparing to assist such patients by automatically synthesizing “personalized knowledge-mining workflows”. The Web interface developed by [38] utilizes a combination of semantic text-mining algorithms that extract the concepts from any Web page a patient is reading together with a local database of their personal medical information, eg, drugs prescribed and/or (eventually) personalized DNA sequence. The tool then combines these concepts and data with globally distributed expert knowledge and “mashes up” all of this information to provide personalized hints and guidelines within that Web page.…”

Section: Sensors “Smart” Technologies and “Expert Patients”mentioning

confidence: 99%

The Emergent Discipline of Health Web Science

Luciano¹,

Cumming²,

Wilkinson³

et al. 2013

J Med Internet Res

Self Cite

View full text Add to dashboard Cite

The transformative power of the Internet on all aspects of daily life, including health care, has been widely recognized both in the scientific literature and in public discourse. Viewed through the various lenses of diverse academic disciplines, these transformations reveal opportunities realized, the promise of future advances, and even potential problems created by the penetration of the World Wide Web for both individuals and for society at large. Discussions about the clinical and health research implications of the widespread adoption of information technologies, including the Internet, have been subsumed under the disciplinary label of Medicine 2.0. More recently, however, multi-disciplinary research has emerged that is focused on the achievement and promise of the Web itself, as it relates to healthcare issues. In this paper, we explore and interrogate the contributions of the burgeoning field of Web Science in relation to health maintenance, health care, and health policy. From this, we introduce Health Web Science as a subdiscipline of Web Science, distinct from but overlapping with Medicine 2.0. This paper builds on the presentations and subsequent interdisciplinary dialogue that developed among Web-oriented investigators present at the 2012 Medicine 2.0 Conference in Boston, Massachusetts.

show abstract

The SADI Personal Health Lens: A Web Browser-Based System for Identifying Personally Relevant Drug Interactions

Cited by 10 publications

References 17 publications

Toward a complete dataset of drug–drug interaction information from publicly available sources

Toward a complete dataset of drug–drug interaction information from publicly available sources

Analysing Syntactic Regularities and Irregularities in SNOMED-CT

The Emergent Discipline of Health Web Science

Contact Info

Product

Resources

About