Building a Transnational Biosurveillance Network Using Semantic Web Technologies: Requirements, Design, and Preliminary Evaluation

Teodoro, Douglas; Pasche, Emilie; Gobeill, Julien; Emonet, Stéphane; Ruch, Patrick; Lovis, Christian

doi:10.2196/jmir.2043

Cited by 33 publications

(29 citation statements)

References 35 publications

(44 reference statements)

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“…In this diagram, 3 major components, ie, the front-end module, the middleware module, and the back-end services including the database servers are shown. The front-end module handles user interfaces via Web browsers [23] and establishes the users’ sessions with the authentication server. The server validates users’ authentications and authorizations.…”

Section: Methodsmentioning

confidence: 99%

Web-Based Newborn Screening System for Metabolic Diseases: Machine Learning Versus Clinicians

Chen

Hsieh²,

Hsu

et al. 2013

J Med Internet Res

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BackgroundA hospital information system (HIS) that integrates screening data and interpretation of the data is routinely requested by hospitals and parents. However, the accuracy of disease classification may be low because of the disease characteristics and the analytes used for classification.ObjectiveThe objective of this study is to describe a system that enhanced the neonatal screening system of the Newborn Screening Center at the National Taiwan University Hospital. The system was designed and deployed according to a service-oriented architecture (SOA) framework under the Web services .NET environment. The system consists of sample collection, testing, diagnosis, evaluation, treatment, and follow-up services among collaborating hospitals. To improve the accuracy of newborn screening, machine learning and optimal feature selection mechanisms were investigated for screening newborns for inborn errors of metabolism.MethodsThe framework of the Newborn Screening Hospital Information System (NSHIS) used the embedded Health Level Seven (HL7) standards for data exchanges among heterogeneous platforms integrated by Web services in the C# language. In this study, machine learning classification was used to predict phenylketonuria (PKU), hypermethioninemia, and 3-methylcrotonyl-CoA-carboxylase (3-MCC) deficiency. The classification methods used 347,312 newborn dried blood samples collected at the Center between 2006 and 2011. Of these, 220 newborns had values over the diagnostic cutoffs (positive cases) and 1557 had values that were over the screening cutoffs but did not meet the diagnostic cutoffs (suspected cases). The original 35 analytes and the manifested features were ranked based on F score, then combinations of the top 20 ranked features were selected as input features to support vector machine (SVM) classifiers to obtain optimal feature sets. These feature sets were tested using 5-fold cross-validation and optimal models were generated. The datasets collected in year 2011 were used as predicting cases.ResultsThe feature selection strategies were implemented and the optimal markers for PKU, hypermethioninemia, and 3-MCC deficiency were obtained. The results of the machine learning approach were compared with the cutoff scheme. The number of the false positive cases were reduced from 21 to 2 for PKU, from 30 to 10 for hypermethioninemia, and 209 to 46 for 3-MCC deficiency.ConclusionsThis SOA Web service–based newborn screening system can accelerate screening procedures effectively and efficiently. An SVM learning methodology for PKU, hypermethioninemia, and 3-MCC deficiency metabolic diseases classification, including optimal feature selection strategies, is presented. By adopting the results of this study, the number of suspected cases could be reduced dramatically.

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Section: Methodsmentioning

confidence: 99%

Web-Based Newborn Screening System for Metabolic Diseases: Machine Learning Versus Clinicians

Chen

Hsieh²,

Hsu

et al. 2013

J Med Internet Res

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“…Queries obtained within a few seconds provide real-time information about antimicrobial resistance that is accurate, comparable to data from EARS-Net. This or similar platforms can serve as the backbone of future multisite and multinational surveillance networks that allow rapid detection of emerging resistance to antibiotics, and guide immediate infection control and public health actions 42 . Also harnessing the power of bioinformatics, an alternative approach to monitor trends in antibiotic resistance proposes the use of automated semantic and scientometric analysis of PubMed entries.…”

Section: Conclusion: Challenges and Opportunities In Surveillancementioning

confidence: 99%

The role of surveillance systems in confronting the global crisis of antibiotic-resistant bacteria

Pérez

Villegas

2015

Current Opinion in Infectious Diseases

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Purpose of Review It is widely accepted that infection control, advanced diagnostics, and novel therapeutics are crucial to mitigate the impact of antibiotic-resistant bacteria. The role of global, national and regional surveillance systems as part of the response to the challenge posed by antibiotic resistance is not sufficiently highlighted. We provide an overview of contemporary surveillance programs, with emphasis on Gram-negative bacteria. Recent Findings The World Health Organization and public health agencies in Europe and the United States recently published comprehensive surveillance reports. These highlight the emergence and dissemination of carbapenem-resistant Enterobacteriaceae (CRE) and other multidrug resistant Gram-negative bacteria. In Israel, public health action to control CRE, especially Klebsiella pneumoniae carbapenemase (KPC) producing-Klebsiella pneumoniae, has advanced together with a better understanding of its epidemiology. Surveillance models adapted to the requirements and capacities of each country are in development. Summary Robust surveillance systems are essential to combat antibiotic resistance, and need to emphasize a “One Health” approach. Refinements in surveillance will come from advances in bioinformatics and genomics that permit the integration of global and local information about antibiotic consumption in humans and animals, molecular mechanisms of resistance, and bacterial genotyping.

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“…Text-mining and data-mining methods are then applied to acquire new knowledge that is stored in a Medical Knowledge Repository (MKR). Finally, this knowledge is used to provide better-quality health care through different clinical applications, such as decision support and trend monitoring [27]. The MKR is deployed in each participating clinical center and can be shared within the DebugIT Linked Data infrastructure.…”

Section: Introductionmentioning

confidence: 99%

Assisted Knowledge Discovery for the Maintenance of Clinical Guidelines

et al. 2013

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BackgroundImproving antibiotic prescribing practices is an important public-health priority given the widespread antimicrobial resistance. Establishing clinical practice guidelines is crucial to this effort, but their development is a complex task and their quality is directly related to the methodology and source of knowledge used.ObjectiveWe present the design and the evaluation of a tool (KART) that aims to facilitate the creation and maintenance of clinical practice guidelines based on information retrieval techniques.MethodsKART consists of three main modules 1) a literature-based medical knowledge extraction module, which is built upon a specialized question-answering engine; 2) a module to normalize clinical recommendations based on automatic text categorizers; and 3) a module to manage clinical knowledge, which formalizes and stores clinical recommendations for further use. The evaluation of the usability and utility of KART followed the methodology of the cognitive walkthrough.ResultsKART was designed and implemented as a standalone web application. The quantitative evaluation of the medical knowledge extraction module showed that 53% of the clinical recommendations generated by KART are consistent with existing clinical guidelines. The user-based evaluation confirmed this result by showing that KART was able to find a relevant antibiotic for half of the clinical scenarios tested. The automatic normalization of the recommendation produced mixed results among end-users.ConclusionsWe have developed an innovative approach for the process of clinical guidelines development and maintenance in a context where available knowledge is increasing at a rate that cannot be sustained by humans. In contrast to existing knowledge authoring tools, KART not only provides assistance to normalize, formalize and store clinical recommendations, but also aims to facilitate knowledge building.

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Building a Transnational Biosurveillance Network Using Semantic Web Technologies: Requirements, Design, and Preliminary Evaluation

Cited by 33 publications

References 35 publications

Web-Based Newborn Screening System for Metabolic Diseases: Machine Learning Versus Clinicians

Web-Based Newborn Screening System for Metabolic Diseases: Machine Learning Versus Clinicians

The role of surveillance systems in confronting the global crisis of antibiotic-resistant bacteria

Assisted Knowledge Discovery for the Maintenance of Clinical Guidelines

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