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Antimicrobial resistance has reached globally alarming levels and is becoming a major public health threat. Lack of efficacious antimicrobial resistance surveillance systems was identified as one of the causes of increasing resistance, due to the lag time between new resistances and alerts to care providers. Several initiatives to track drug resistance evolution have been developed. However, no effective real-time and source-independent antimicrobial resistance monitoring system is available publicly.
To design and implement an architecture that can provide real-time and source-independent antimicrobial resistance monitoring to support transnational resistance surveillance. In particular, we investigated the use of a Semantic Web-based model to foster integration and interoperability of interinstitutional and cross-border microbiology laboratory databases.
Following the agile software development methodology, we derived the main requirements needed for effective antimicrobial resistance monitoring, from which we proposed a decentralized monitoring architecture based on the Semantic Web stack. The architecture uses an ontology-driven approach to promote the integration of a network of sentinel hospitals or laboratories. Local databases are wrapped into semantic data repositories that automatically expose local computing-formalized laboratory information in the Web. A central source mediator, based on local reasoning, coordinates the access to the semantic end points. On the user side, a user-friendly Web interface provides access and graphical visualization to the integrated views.
We designed and implemented the online Antimicrobial Resistance Trend Monitoring System (ARTEMIS) in a pilot network of seven European health care institutions sharing 70+ million triples of information about drug resistance and consumption. Evaluation of the computing performance of the mediator demonstrated that, on average, query response time was a few seconds (mean 4.3, SD 0.1×102 seconds). Clinical pertinence assessment showed that resistance trends automatically calculated by ARTEMIS had a strong positive correlation with the European Antimicrobial Resistance Surveillance Network (EARS-Net) (ρ = .86,
We introduce a distributed monitoring architecture that can be used to build transnational antimicrobial resistance surveillance networks. Results indicated that the Semantic Web-based approach provided an efficient and reliable solution for development of eHealth architectures that enable online antimicrobial resistance monitoring from heterogeneous data sources. In future, we expect that more health care institutions can join the ARTEMIS network so that it can provide a large European and wider biosurveillance network that can be used to detect emerging bacterial resistance in a multinational context and support public health actions.
Since their discovery, antibiotics have proved powerful for the control of bacterial infections. However, because of multifactorial causes, especially the widespread use of antibiotics in medicine, animal husbandry, and agriculture, pathogens have developed increasing resistance to many effective drugs [
In addition to well-known drug resistance cases such as
Over a decade ago, Monnet et al[
The primary aim of this study was to develop a framework for transnational antimicrobial resistance monitoring, featuring real-time access to laboratory information and being generic with respect to data sources, in order to support multinational resistance surveillance. The secondary aim of the study was to investigate the use of Semantic Web-based architecture in the integration and interoperability of interinstitutional and cross-border databases to support such a framework. To fulfill these aims, we designed the Antimicrobial Resistance Trend Monitoring System (ARTEMIS). ARTEMIS architecture illustrates how Semantic Web technologies can support online monitoring of antimicrobial resistance trends in heterogeneous networks of health care institutions. It demonstrates how semantically interoperable end points can provide on-demand information on resistance evolution. Furthermore, it describes ways to automate the monitoring process through a state-of-the-art clinical data integration system, which provides mechanisms to adapt to existing electronic health records and laboratory information systems. The architecture is validated according to performance and clinical pertinence.
This paper addresses a large audience, from engineers who have Semantic Web techniques in mind to public health authorities, by showing the results of applying Semantic Web technologies to one of the most crucial current public health challenges: building a global surveillance system for antimicrobial resistances. Here we discuss the technical framework of the project, a technical evaluation, and the quality of the system compared with existing surveillance networks.
Several projects have been implemented to provide monitoring and surveillance of antimicrobial resistance evolution in a European context. WHONET was one of the first initiatives to standardize and aggregate results from laboratories in a cross-country environment[
The most successful European surveillance project is the European Antimicrobial Resistance Surveillance System[
A few other public initiatives were introduced in parallel. In 1998, the European Society of Biomodulation and Chemotherapy created the European Surveillance of Antibiotic Resistance project[
In contrast to the previous initiatives, The Surveillance Network is a corporate-funded surveillance project[
ARTEMIS was developed as part of the Detecting and Eliminating Bacteria Using Information Technology (DebugIT) project, which is funded by the European Union Seventh Framework Programme [
The DebugIT architecture (
Architecture of the Detecting and Eliminating Bacteria Using Information Technology (DebugIT) framework. Components of the architecture, such as the clinical data repository (CDR), knowledge repository (KR), decision support system (DSS), and monitoring system (MS), are interconnected using the HTTP/SPARQL protocol through the Internet bus. Messages are transferred in the RDF format, and ontologies formalize the data model and content.
Experts from the DebugIT project with different backgrounds, including infectiologists, epidemiologists, computer scientists, knowledge engineers, and eHealth service providers, were involved in the design of ARTEMIS. Over the course of 2 years, we held weekly meetings with these experts to discuss the status of the tasks involved in the system development [
To provide a monitoring system that can be effectively used in the fight against antimicrobial resistance, we derived the following six main requirements based on the published literature and on the expertise of the DebugIT consortium.
All public European supranational monitoring systems provide resistance information in batch mode—that is, data are collected into batches of laboratory tests and processed periodically, usually on a yearly frequency. While online resistance information is useful on a daily basis at local levels, recent infectious pandemic threats have shown how important this information would be at a multinational level for decision makers. Thus, changing this paradigm to online trends is crucial for antimicrobial resistance surveillance, especially for early warning of emerging resistance trends [
Increasing antibiotic resistance is a worldwide public health concern, and for its effective combat, a successful surveillance system has to offer multinational resistance information[
Sharing biomedical data raises several ethical concerns[
Most of the available systems do not use formalized biomedical data models, nor computable terminologies and ontologies. As a result, the process of extracting resistance information and data analysis in a heterogeneous environment is done manually or semiautomatically. In addition to the overhead work, the lack of formal conceptualization of the raw laboratory data can have a negative influence on the quality of the data.
To be operatively used by health care professionals, whose working environment is recognized to be very time constrained, eHealth systems must provide a fast response time.
Automatic extraction of antimicrobial resistance trends from heterogeneous data sources poses several challenges to accurate data analysis, including concept ambiguity and the common denominator, which can degrade the quality of the examination. However, especially if the system is used by clinicians at the point of care, the accuracy of the results must be equivalent to those obtained by semiautomatic processes, where data cleansing and audit are performed prior to integration and interpretation.
To fulfill the ARTEMIS desiderata, we envisaged the system according to the Semantic Web-complying architecture presented in
In the architecture’s data model layer, local laboratory databases are connected online to semantic-aware end points, the local clinical data repositories (lCDRs) [
A more detailed description of the system model is given in
Antimicrobial Resistance Trend Monitoring System (ARTEMIS) architecture. (a) Ontology components. Models: data definition ontology (DDO), DebugIT Core Ontology (DCO), and interface ontology (IO). Mappings: local-terminology-to-DCO (LT2DCO) and global-terminology-to-DCO (T2DCO). (b) Run-time business components. (1) Data layer components are deployed within the demilitarized zone of the health care institution. (2) Controller and view layers contain central services, which are deployed in the Internet. lCDR = local clinical data repository.
To assess ARTEMIS, we connected a network of seven data providers: National Heart Hospital, Sofia, Bulgaria; Les Hôpitaux Universitaires de Genève, Geneva, Switzerland; Georges Pompidou European Hospital, Paris, France; Internetový Pristup Ke Zdravotním Informacím Pacienta, Prague, Czech Republic; Swedish Intensive Care Registry, Sweden; Athens Chest Hospital “Sotiria”, Athens, Greece; and Universitätsklinikum Freiburg, Freiburg, Germany.
We obtained permission to use de-identified data from the ethics committees of the respective participant hospitals. Privacy-sensitive information accessible through the local end points was pseudoanonymized to conform to the European legal and ethical patient data-sharing framework[
Data used in the Antimicrobial Resistance Trend Monitoring System (ARTEMIS).
Data group | Data item | ACHa | HEGPb | HUGc | IZIPd | NHHe | SIRf | UKLFRg |
Demographics | Age | ×h | × | × | × | × | × | −i |
Sex | × | × | × | × | × | × | − | |
Location | Department | − | × | − | − | − | − | × |
Laboratory | Bacteria | × | × | × | − | × | × | × |
Antibiotic | × | × | × | − | × | × | × | |
Specimen | × | × | × | − | × | × | × | |
S.I.R.j | × | × | × | − | × | × | × | |
Medication | Drug | × | × | × | × | × | − | − |
Triples (×106) | 0.05 | 25.20 | 19.87 | 2.79 | 0.02 | 3.81 | 19.10 |
a Athens Chest Hospital “Sotiria.”
b Georges Pompidou European Hospital.
c Les Hôpitaux Universitaires de Genève.
d Internetový Pristup Ke Zdravotním Informacím Pacienta.
e National Heart Hospital.
f Swedish Intensive Care Registry.
g Universitätsklinikum Freiburg.
h Concept available in the local clinical data repository.
i Concept not available in the local clinical data repository.
j Breakpoint values: susceptible (S), intermediate (I), and resistant (R).
The implementation of the functional features defined in the first four design requirements is described at the technical component level using design pattern [
We measured efficiency using the mediator’s query retrieval time for the three aforementioned query templates. Combinations of pathogens, antibiotics, and sample types were applied to vary the queries and thus avoid database caching effects. Results of the local aggregation mode applied in the query mediator were compared with a central aggregation strategy (baseline).
To assess effectiveness, resistance trends extracted using query template 1 were compared with data from two publicly available surveillance systems: EARS-Net and the Sentinel Surveillance of Antibiotic Resistance in Switzerland (SEARCH). We extracted yearly resistance trends for seven key pathogenic bacteria—
We compared results from Georges Pompidou European Hospital, Swedish Intensive Care Registry, and Universitätsklinikum Freiburg with the resistance rates of their respective EARS-Net countries—France, Sweden, and Germany— and results from Les Hôpitaux Universitaires de Genève with SEARCH. We report correlation and equivalence results using the Spearman rank correlation and the two one-sided convolution[
ARTEMIS was implemented and deployed in a pilot network of seven European health care institutions sharing 70+ million triples of antimicrobial resistance information. As
Antimicrobial Resistance Trend Monitoring System (ARTEMIS) interface. The menu on the left displays the interface ontology concepts, which are used to fill in the template parameters. Each of the view tabs represents a different query template. The data visualization interface displays several graphical representations to provide a comprehensive view of the data.
In this section, we present design patterns describing the main functional features of the online distributed monitoring system.
The system shall provide online information.
In the architecture presented in
In ARTEMIS, the technical interoperability with the different data sources is provided by D2R [
Local clinical data repository (lCDR) deployment and population model. (a) Production data are extracted daily to a local mirror database, which is “sparqlized” by an SQL-to-RDF engine. (b) RDF view is created directly on top of the legacy system. Data are anonymized on the fly.
The system shall provide aggregated information from numerous international sources.
The technical and semantic heterogeneity within models and concepts from different clinical data sources poses an important barrier for data aggregation and analysis. ARTEMIS architecture relies on a layer of semantically formalized end points, the lCDRs, to solve part of the integration problem. These end points provide a first level of interoperability, modeling the local systems and the data content and providing a common protocol to access data, the SPARQL protocol. The semantic mediator designed in the controller layer builds on top of the lCDR layer and allows the creation of homogeneous aggregated views over the distributed data sources. Thus, the system becomes a grid of semantic-aware sentinels that provide antimicrobial resistance information from heterogeneous supranational data sources.
In ARTEMIS, the lCDRs are provided by RDF-like stores to create the first semantic layer on top of the local databases. The data definition ontologies formalize the local end points and expose linkable data on the Web. The Jena Framework is used for querying the remote lCDRs and for reasoning over the RDF models.
The system shall not store data centrally.
ARTEMIS changes the centralized integration paradigm used in antimicrobial resistance surveillance. Unlike other systems[
In the model–view–control pattern [
The system shall implement a formal and semantic-aware data model.
In a multinational environment, the contents of electronic health records and laboratory information systems are expressed in several languages and different terminologies. Additionally, spelling mistakes and abbreviations are common in concept definitions. These ambiguities reduce the quality of the statistical analysis. To have unified semantics across the different data sources, in ARTEMIS’s knowledge model (
The hybrid ontology-driven interoperability mapping model. White elements represent local-level concepts and blue elements represent shared knowledge. (a) Local entity-relationship schemata are formalized by the data definition ontologies (DDOs). Mappings between DDO data elements and DebugIT Core Ontology (DCO) link local concepts to the global knowledge. (b) Example of a semantic mapping: concept map diagram (left) and RDF/Notation3 representation (right).
In ARTEMIS, standard terminologies such as the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT), the WHO’s Anatomical Therapeutic Chemical (WHO-ATC) classification system, and the Universal Protein Resource (UniProt/NEWT) are mapped to DebugIT Core Ontology using the Simple Knowledge Organization System (SKOS) ontology [
We assessed the mediator’s SPARQL query performance for query templates 1, 2, and 3. A query mix composed of 225 unique queries, spanning 4 years in daily, monthly, and yearly periods, were used. Each query mix was submitted 10 times against the seven end points.
Arithmetic (ta) and geometric (tg) mean (SD) execution times for the two query mediation strategies: local (Antimicrobial Resistance Trend Monitoring System [ARTEMIS]) versus central (baseline) reasoning.
Template | Number of |
ARTEMIS | Baseline | ||
ta (seconds) | tg (seconds) | ta (seconds) | tg (seconds) | ||
1 | 75 | 8.4 (0.1×102) | 4.2 (0.1) | 311.0 (0.9×103) | 308.3 (0.1) |
2 | 75 | 2.3 (0.6×10) | 1.3 (0.1) | 74.7 (0.6×102) | 72.1 (0.1) |
3 | 75 | 2.0 (0.2×10) | 1.7 (0.1) | 5.9 (0.8×10) | 2.7 (0.1) |
All | 225 | 4.3 (0.1×102) | 2.1 (0.1) | 130.5 (0.1×103) | 39.2 (0.1) |
Query performance. Response time and rows retrieved by template (1-3) and aggregation period. As the number of rows retrieved increases, the response time tends also to increase.
Following the data selection criterion, we created 221 queries for EARS-Net and 153 for SEARCH based on template 1.
The within-country geometric standard deviation of EARS-Net was σears = 0.130. This value was extrapolated to the similarity region Δ ( Δ = σears) of the two one-sided convolution test.
Resistance rate geometric mean (SD) and correlation results.
Number of |
Resistance rate | ρ |
|
||
EARS-Neta | SEARCHb | ARTEMISc | |||
221 | 0.032 (0.002×102) | NAd | 0.038 (0.002×102) | .86 | <.001 |
153 | NA | 0.042 (0.001×102) | 0.053 (0.002×102) | .84 | <.001 |
a European Antimicrobial Resistance Surveillance Network.
b Sentinel Surveillance of Antibiotic Resistance in Switzerland.
c Antimicrobial Resistance Trend Monitoring System.
d Not applicable.
Antimicrobial Resistance Trend Monitoring System (ARTEMIS) vs European Antimicrobial Resistance Surveillance Network (EARS-Net). (a) Resistance rates (n = 221). Black line indicates an exact match (100% equivalence). Gray line indicates best fit. Gray dashed lines indicate Δ = ±0.130. (b) Resistance rates without outliers (n = 213). (c) Gray vertical dashed lines indicate similarity region Δ. Gray horizontal bars indicate two one-sided convolution confidence interval (CI). 95% CIa 0–0.030 (
Antimicrobial Resistance Trend Monitoring System (ARTEMIS) vs Sentinel Surveillance of Antibiotic Resistance in Switzerland (SEARCH). (a) Resistance rates (n = 153). Black line indicates exact match (100% equivalence). Gray line indicates best fit. Gray dashed lines indicate Δ = ±0.042. (b) Resistance rates without outliers (n = 143). (c) Gray vertical dashed lines indicate similarity region Δ. Gray horizontal bars indicate two one-sided convolution confidence interval (CI). 95% CIa 0–0.052 (
In this paper, we present an online and source-independent architecture that enables monitoring of multinational microbiology databases. The system was implemented and deployed in a pilot surveillance network distributed across Europe. From the results, one can see that Semantic Web-based architectures such as that of ARTEMIS are suitable to automate the integration and interoperability of distributed microbiology laboratory data sources. Therefore, it can be used to enable automatic access to antimicrobial resistance information in a transnational context and foster real-time multinational biosurveillance. The architecture is able to interoperate heterogeneous networks via the use of semantic maps that account for local specificity. The data integration process is performed on the fly using standard end points powered with RDF/SPARQL communication, which are mediated by a central engine. The local end points are directly connected to the laboratories’ databases and as such are able to provide (near) real-time resistance information, while avoiding centralization of the data.
The data integration architecture proposed in ARTEMIS is distinct from existing antimicrobial resistance surveillance systems [
ARTEMIS uses open Semantic Web technologies to provide technical and semantic interoperability. Semantic data sources create a common technical layer over the local microbiology databases, which can be accessed through a standard query protocol (SPARQL). Since local end points are fully formalized and accessible through the Web, they can be linked to external Web resources, such as the Linked Life Data[
Finally, the powerful query interface allied with the availability of near real-time results makes ARTEMIS not only useful to bodies concerned with supranational resistance but also potentially beneficial to local needs, especially if connected to online prescribing systems for empirical treatments. In addition, this local application might facilitate the maintenance of the system by health care institutions. As Goble and Stevens discussed [
All SPARQL performance benchmarks presented in the literature are focused on local single-source servers[
The preference for an SQL-to-RDF engine [
Existing surveillance systems normally use semiautomatic methods to extract antimicrobial resistance rates[
A difference in concept definition between ARTEMIS and the reference systems negatively affected the results. The majority of outliers (18 out of 33) presented in
Finally, in statistical analysis, care should be taken with duplicate tests. If all apparent duplicates are ignored indiscriminately, information may be omitted, such as nosocomial infection, whereas inclusion of all tests may skew the results, usually toward augmentation of resistance [
In an ontology-based integration system, automatic mapping from global to local ontologies using first-order logic reasoners creates logical inconsistencies because knowledge from the various local ontologies cannot be completely reconciled in the global model[
Aligning multinational microbiology laboratory results presents several issues. For example, it has been shown[
We designed, implemented, and deployed the ARTEMIS architecture in a small-scale biosurveillance network of European hospitals. Results indicate that the distributed monitoring architecture introduced here can potentially be used to build transnational antimicrobial resistance surveillance networks. The architecture proved to be efficient and reliable, while complying with local legal and regulatory frameworks. The Semantic Web-based approach proved to be an effective solution for development of eHealth architectures that enable online antimicrobial resistance monitoring from heterogeneous data sources. In the future, we plan to investigate local model mediation, paving the way to a more easily maintainable system. We expect that new health care institutions can join the network so that it can provide clinicians and decision makers with a missing tool to tackle the growing threat of rising emergent infectious diseases and antibiotic resistance patterns.
Architecture design.
Equivalence test.
Antimicrobial Resistance Trend Monitoring System
confidence interval
Detecting and Eliminating Bacteria Using Information Technology
European Antimicrobial Resistance Surveillance Network
local clinical data repository
Resource Description Framework
Sentinel Surveillance of Antibiotic Resistance in Switzerland
Simple Knowledge Organization System
Systematized Nomenclature of Medicine-Clinical Terms
Simple Protocol and RDF Query Language
World Health Organization
World Health Organization- Anatomical Therapeutic Chemical
This work is funded by the DebugIT project of the European Union Seventh Framework Programme grant agreement ICT-2007.5.2-217139.
None declared.