Douglas Teodoro

Building a transnational biosurveillance network using semantic web technologies: requirements, design, and preliminary evaluation.

Background 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. Objective 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. Methods 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. Results 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, P < .001) and the Sentinel Surveillance of Antibiotic Resistance in Switzerland (SEARCH) (ρ = .84, P < .001) systems. Furthermore, mean resistance rates extracted by ARTEMIS were not significantly different from those of either EARS-Net (∆ = ±0.130; 95% confidence interval –0 to 0.030; P < .001) or SEARCH (∆ = ±0.042; 95% confidence interval –0.004 to 0.028; P = .004). Conclusions 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.

Simple pre and post processing strategies for patent searching in CLEF intellectual property track 2009

The objective of the 2009 CLEF-IP Track was to find documents that constitute prior art for a given patent. We explored a wide range of simple preprocessing and post-processing strategies, using Mean Average Precision (MAP) for evaluation purposes. Once determined the best document representation, we tuned a classical Information Retrieval engine in order to perform the retrieval step. Finally, we explored two different post-processing strategies. In our experiments, using the complete IPC codes for filtering purposes led to greater improvements than using 4-digits IPC codes. The second postprocessing strategy was to exploit the citations of retrieved patents in order to boost scores of cited patents. Combining all selected strategies, we computed optimal runs that reached a MAP of 0.122 for the training set, and a MAP of 0.129 for the official 2009 CLEF-IP XL set.

Comparing the Performance of NoSQL Approaches for Managing Archetype-Based Electronic Health Record Data

This study provides an experimental performance evaluation on population-based queries of NoSQL databases storing archetype-based Electronic Health Record (EHR) data. There are few published studies regarding the performance of persistence mechanisms for systems that use multilevel modelling approaches, especially when the focus is on population-based queries. A healthcare dataset with 4.2 million records stored in a relational database (MySQL) was used to generate XML and JSON documents based on the openEHR reference model. Six datasets with different sizes were created from these documents and imported into three single machine XML databases (BaseX, eXistdb and Berkeley DB XML) and into a distributed NoSQL database system based on the MapReduce approach, Couchbase, deployed in different cluster configurations of 1, 2, 4, 8 and 12 machines. Population-based queries were submitted to those databases and to the original relational database. Database size and query response times are presented. The XML databases were considerably slower and required much more space than Couchbase. Overall, Couchbase had better response times than MySQL, especially for larger datasets. However, Couchbase requires indexing for each differently formulated query and the indexing time increases with the size of the datasets. The performances of the clusters with 2, 4, 8 and 12 nodes were not better than the single node cluster in relation to the query response time, but the indexing time was reduced proportionally to the number of nodes. The tested XML databases had acceptable performance for openEHR-based data in some querying use cases and small datasets, but were generally much slower than Couchbase. Couchbase also outperformed the response times of the relational database, but required more disk space and had a much longer indexing time. Systems like Couchbase are thus interesting research targets for scalable storage and querying of archetype-based EHR data when population-based use cases are of interest.

Assisted knowledge discovery for the maintenance of clinical guidelines

Background Improving 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. Objective We 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. Methods KART 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. Results KART 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. Conclusions We 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.

Empirical mode decomposition and k-nearest embedding vectors for timely analyses of antibiotic resistance trends.

Background Antibiotic resistance is a major worldwide public health concern. In clinical settings, timely antibiotic resistance information is key for care providers as it allows appropriate targeted treatment or improved empirical treatment when the specific results of the patient are not yet available. Objective To improve antibiotic resistance trend analysis algorithms by building a novel, fully data-driven forecasting method from the combination of trend extraction and machine learning models for enhanced biosurveillance systems. Methods We investigate a robust model for extraction and forecasting of antibiotic resistance trends using a decade of microbiology data. Our method consists of breaking down the resistance time series into independent oscillatory components via the empirical mode decomposition technique. The resulting waveforms describing intrinsic resistance trends serve as the input for the forecasting algorithm. The algorithm applies the delay coordinate embedding theorem together with the k-nearest neighbor framework to project mappings from past events into the future dimension and estimate the resistance levels. Results The algorithms that decompose the resistance time series and filter out high frequency components showed statistically significant performance improvements in comparison with a benchmark random walk model. We present further qualitative use-cases of antibiotic resistance trend extraction, where empirical mode decomposition was applied to highlight the specificities of the resistance trends. Conclusion The decomposition of the raw signal was found not only to yield valuable insight into the resistance evolution, but also to produce novel models of resistance forecasters with boosted prediction performance, which could be utilized as a complementary method in the analysis of antibiotic resistance trends.

Challenges in the Implementation of a Mobile Application in Clinical Practice: Case Study in the Context of an Application that Manages the Daily Interventions of Nurses.

Background Working in a clinical environment requires unfettered mobility. This is especially true for nurses who are always on the move providing patients’ care in different locations. Since the introduction of clinical information systems in hospitals, this mobility has often been considered hampered by interactions with computers. The popularity of personal mobile assistants such as smartphones makes it possible to gain easy access to clinical data anywhere. Objective To identify the challenges involved in the deployment of clinical applications on handheld devices and to share our solutions to these problems. Methods A team of experts underwent an iterative development process of a mobile application prototype that aimed to improve the mobility of nurses during their daily clinical activities. Through the process, challenges inherent to mobile platforms have emerged. These issues have been classified, focusing on factors related to ensuring information safety and quality, as well as pleasant and efficient user experiences. Results The team identified five main challenges related to the deployment of clinical mobile applications and presents solutions to overcome each of them: (1) Financial: Equipping every care giver with a new mobile device requires substantial investment that can be lowered if users use their personal device instead, (2) Hardware: The constraints inherent to the clinical environment made us choose the mobile device with the best tradeoff between size and portability, (3) Communication: the connection of the mobile application with any existing clinical information systems (CIS) is insured by a bridge formatting the information appropriately, (4) Security: In order to guarantee the confidentiality and safety of the data, the amount of data stored on the device is minimized, and (5) User interface: The design of our user interface relied on homogeneity, hierarchy, and indexicality principles to prevent an increase in data acquisition errors. Conclusions The introduction of nomadic computing often raises enthusiastic reactions from users, but several challenges due to specific constraints of mobile platforms must be overcome. The ease of development of mobile applications and their rapid spread should not overshadow the real challenges of clinical applications and the potential threats for patient safety and the liability of people and organizations using them. For example, careful attention must be given to the overall architecture of the system and to user interfaces. If these precautions are not taken, it can easily lead to unexpected failures such as an increased number of input errors, loss of data, or decreased efficiency.

KART, a knowledge authoring and refinement tool for clinical guidelines development

Optimal antibiotic prescriptions rely on evidence-based clinical guidelines, but creating such guidelines requires a time-consuming systematic review of the literature. We aim at facilitating this process by proposing an innovative tool to extract antibiotic treatments from the literature.

ORBDA: An openEHR benchmark dataset for performance assessment of electronic health record servers

The openEHR specifications are designed to support implementation of flexible and interoperable Electronic Health Record (EHR) systems. Despite the increasing number of solutions based on the openEHR specifications, it is difficult to find publicly available healthcare datasets in the openEHR format that can be used to test, compare and validate different data persistence mechanisms for openEHR. To foster research on openEHR servers, we present the openEHR Benchmark Dataset, ORBDA, a very large healthcare benchmark dataset encoded using the openEHR formalism. To construct ORBDA, we extracted and cleaned a de-identified dataset from the Brazilian National Healthcare System (SUS) containing hospitalisation and high complexity procedures information and formalised it using a set of openEHR archetypes and templates. Then, we implemented a tool to enrich the raw relational data and convert it into the openEHR model using the openEHR Java reference model library. The ORBDA dataset is available in composition, versioned composition and EHR openEHR representations in XML and JSON formats. In total, the dataset contains more than 150 million composition records. We describe the dataset and provide means to access it. Additionally, we demonstrate the usage of ORBDA for evaluating inserting throughput and query latency performances of some NoSQL database management systems. We believe that ORBDA is a valuable asset for assessing storage models for openEHR-based information systems during the software engineering process. It may also be a suitable component in future standardised benchmarking of available openEHR storage platforms.

Integration of biomedical data using federated databases

The expansion of biomedical knowledge, reduction in computing costs and spread of IT facilities have conducted to an escalation of the biomedical electronic data. However, these data are rarely integrated and analysed because of the insufficiency of specialised tools. This paper presents a pilot system that will be used in the European FP7 DebugIT project to integrate biomedical data from several healthcare centres across Europe. The system aims at solving complex problems derived from the technical and semantic heterogeneity intrinsic to these kinds of data sources as well as from the absence of reliability of the distributed system.