Sharing Data and Analytical Resources Securely in a Biomedical Research Grid Environment

Objectives

To develop a security infrastructure to support controlled and secure access to data and analytical resources in a biomedical research Grid environment, while facilitating resource sharing among collaborators.

Design

A Grid security infrastructure, called Grid Authentication and Authorization with Reliably Distributed Services (GAARDS), is developed as a key architecture component of the NCI-funded cancer Biomedical Informatics Grid (caBIG^™). The GAARDS is designed to support in a distributed environment 1) efficient provisioning and federation of user identities and credentials; 2) group-based access control support with which resource providers can enforce policies based on community accepted groups and local groups; and 3) management of a trust fabric so that policies can be enforced based on required levels of assurance.

Measurements

GAARDS is implemented as a suite of Grid services and administrative tools. It provides three core services: Dorian for management and federation of user identities, Grid Trust Service for maintaining and provisioning a federated trust fabric within the Grid environment, and Grid Grouper for enforcing authorization policies based on both local and Grid-level groups.

Results

The GAARDS infrastructure is available as a stand-alone system and as a component of the caGrid infrastructure. More information about GAARDS can be accessed at http://www.cagrid.org.

Conclusions

GAARDS provides a comprehensive system to address the security challenges associated with environments in which resources may be located at different sites, requests to access the resources may cross institutional boundaries, and user credentials are created, managed, revoked dynamically in a de-centralized manner.     

Knowledge-based biomedical word sense disambiguation: an evaluation and application to clinical document classification

Background

Word sense disambiguation (WSD) methods automatically assign an unambiguous concept to an ambiguous term based on context, and are important to many text-processing tasks. In this study we developed and evaluated a knowledge-based WSD method that uses semantic similarity measures derived from the Unified Medical Language System (UMLS) and evaluated the contribution of WSD to clinical text classification.

Methods

We evaluated our system on biomedical WSD datasets and determined the contribution of our WSD system to clinical document classification on the 2007 Computational Medicine Challenge corpus.

Results

Our system compared favorably with other knowledge-based methods. Machine learning classifiers trained on disambiguated concepts significantly outperformed those trained using all concepts.

Conclusions

We developed a WSD system that achieves high disambiguation accuracy on standard biomedical WSD datasets and showed that our WSD system improves clinical document classification.

Data sharing

We integrated our WSD system with MetaMap and the clinical Text Analysis and Knowledge Extraction System, two popular biomedical natural language processing systems. All codes required to reproduce our results and all tools developed as part of this study are released as open source, available under http://code.google.com/p/ytex.     

Epilepsy and seizure ontology: towards an epilepsy informatics infrastructure for clinical research and patient care

Objective

Epilepsy encompasses an extensive array of clinical and research subdomains, many of which emphasize multi-modal physiological measurements such as electroencephalography and neuroimaging. The integration of structured, unstructured, and signal data into a coherent structure for patient care as well as clinical research requires an effective informatics infrastructure that is underpinned by a formal domain ontology.

Methods

We have developed an epilepsy and seizure ontology (EpSO) using a four-dimensional epilepsy classification system that integrates the latest International League Against Epilepsy terminology recommendations and National Institute of Neurological Disorders and Stroke (NINDS) common data elements. It imports concepts from existing ontologies, including the Neural ElectroMagnetic Ontologies, and uses formal concept analysis to create a taxonomy of epilepsy syndromes based on their seizure semiology and anatomical location.

Results

EpSO is used in a suite of informatics tools for (a) patient data entry, (b) epilepsy focused clinical free text processing, and (c) patient cohort identification as part of the multi-center NINDS-funded study on sudden unexpected death in epilepsy. EpSO is available for download at http://prism.case.edu/prism/index.php/EpilepsyOntology.

Discussion

An epilepsy ontology consortium is being created for community-driven extension, review, and adoption of EpSO. We are in the process of submitting EpSO to the BioPortal repository.

Conclusions

EpSO plays a critical role in informatics tools for epilepsy patient care and multi-center clinical research.     

Recommending MeSH terms for annotating biomedical articles

Background

Due to the high cost of manual curation of key aspects from the scientific literature, automated methods for assisting this process are greatly desired. Here, we report a novel approach to facilitate MeSH indexing, a challenging task of assigning MeSH terms to MEDLINE citations for their archiving and retrieval.

Methods

Unlike previous methods for automatic MeSH term assignment, we reformulate the indexing task as a ranking problem such that relevant MeSH headings are ranked higher than those irrelevant ones. Specifically, for each document we retrieve 20 neighbor documents, obtain a list of MeSH main headings from neighbors, and rank the MeSH main headings using ListNet–a learning-to-rank algorithm. We trained our algorithm on 200 documents and tested on a previously used benchmark set of 200 documents and a larger dataset of 1000 documents.

Results

Tested on the benchmark dataset, our method achieved a precision of 0.390, recall of 0.712, and mean average precision (MAP) of 0.626. In comparison to the state of the art, we observe statistically significant improvements as large as 39% in MAP (p-value <0.001). Similar significant improvements were also obtained on the larger document set.

Conclusion

Experimental results show that our approach makes the most accurate MeSH predictions to date, which suggests its great potential in making a practical impact on MeSH indexing. Furthermore, as discussed the proposed learning framework is robust and can be adapted to many other similar tasks beyond MeSH indexing in the biomedical domain. All data sets are available at: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/indexing.     

Enabling collaborative research using the Biomedical Informatics Research Network (BIRN)

Objective

As biomedical technology becomes increasingly sophisticated, researchers can probe ever more subtle effects with the added requirement that the investigation of small effects often requires the acquisition of large amounts of data. In biomedicine, these data are often acquired at, and later shared between, multiple sites. There are both technological and sociological hurdles to be overcome for data to be passed between researchers and later made accessible to the larger scientific community. The goal of the Biomedical Informatics Research Network (BIRN) is to address the challenges inherent in biomedical data sharing.

Materials and methods

BIRN tools are grouped into ‘capabilities’ and are available in the areas of data management, data security, information integration, and knowledge engineering. BIRN has a user-driven focus and employs a layered architectural approach that promotes reuse of infrastructure. BIRN tools are designed to be modular and therefore can work with pre-existing tools. BIRN users can choose the capabilities most useful for their application, while not having to ensure that their project conforms to a monolithic architecture.

Results

BIRN has implemented a new software-based data-sharing infrastructure that has been put to use in many different domains within biomedicine. BIRN is actively involved in outreach to the broader biomedical community to form working partnerships.

Conclusion

BIRN''s mission is to provide capabilities and services related to data sharing to the biomedical research community. It does this by forming partnerships and solving specific, user-driven problems whose solutions are then available for use by other groups.     

Impact of iron supplementation on schistosomiasis control in Zambian school children in a highly endemic area

Aim

To study impact of once weekly iron supplementation on praziquantel cure rate, Schistosoma haematobium reinfection, and haematological parameters in pupils aged between 9 and 15 years of age in Nchelenge district, Zambia.

Methods

Pupils in the intervention group received once weekly dose of ferrous sulphate at 200mg while those in the control received once weekly vitamin C at 100mg for up to 9 months. Both study groups received a single dose of praziquantel at baseline.

Results

S haematobium reinfection intensity was significantly lower in boys in the intervention group than in boys in the control group at 6 months (P<0.001) and 9 months (P<0.001) of supplementation. Significantly lower S haematobium reinfection intensity was found in girls in the intervention group than in girls in the control group only at 6 months of supplementation (P=0.018). Boys in the intervention group were 42% (Adjusted Risk Ratio =0.58, 95% confidence interval 0.39, 0.86) less likely to be reinfected with S haematobium than in the control group at 6 months follow up.

Conclusion

Once weekly iron supplementation can decrease S haematobium reinfection after 6 months and should be incorporated into school based schistosomiasis control programs in highly endemic areas.

Clinical trials.gov identifier

{"type":"clinical-trial","attrs":{"text":"NCT 00276224","term_id":"NCT00276224"}}NCT 00276224, sponsored by DBL-Institute for Health Research and Development, Denmark.     

Automatic discourse connective detection in biomedical text

Objective

Relation extraction in biomedical text mining systems has largely focused on identifying clause-level relations, but increasing sophistication demands the recognition of relations at discourse level. A first step in identifying discourse relations involves the detection of discourse connectives: words or phrases used in text to express discourse relations. In this study supervised machine-learning approaches were developed and evaluated for automatically identifying discourse connectives in biomedical text.

Materials and Methods

Two supervised machine-learning models (support vector machines and conditional random fields) were explored for identifying discourse connectives in biomedical literature. In-domain supervised machine-learning classifiers were trained on the Biomedical Discourse Relation Bank, an annotated corpus of discourse relations over 24 full-text biomedical articles (∼112 000 word tokens), a subset of the GENIA corpus. Novel domain adaptation techniques were also explored to leverage the larger open-domain Penn Discourse Treebank (∼1 million word tokens). The models were evaluated using the standard evaluation metrics of precision, recall and F1 scores.

Results and Conclusion

Supervised machine-learning approaches can automatically identify discourse connectives in biomedical text, and the novel domain adaptation techniques yielded the best performance: 0.761 F1 score. A demonstration version of the fully implemented classifier BioConn is available at: http://bioconn.askhermes.org.     

Understanding Detection Performance in Public Health Surveillance: Modeling Aberrancy-detection Algorithms

Objective

Statistical aberrancy-detection algorithms play a central role in automated public health systems, analyzing large volumes of clinical and administrative data in real-time with the goal of detecting disease outbreaks rapidly and accurately. Not all algorithms perform equally well in terms of sensitivity, specificity, and timeliness in detecting disease outbreaks and the evidence describing the relative performance of different methods is fragmented and mainly qualitative.

Design

We developed and evaluated a unified model of aberrancy-detection algorithms and a software infrastructure that uses this model to conduct studies to evaluate detection performance. We used a task-analytic methodology to identify the common features and meaningful distinctions among different algorithms and to provide an extensible framework for gathering evidence about the relative performance of these algorithms using a number of evaluation metrics. We implemented our model as part of a modular software infrastructure (Biological Space-Time Outbreak Reasoning Module, or BioSTORM) that allows configuration, deployment, and evaluation of aberrancy-detection algorithms in a systematic manner.

Measurement

We assessed the ability of our model to encode the commonly used EARS algorithms and the ability of the BioSTORM software to reproduce an existing evaluation study of these algorithms.

Results

Using our unified model of aberrancy-detection algorithms, we successfully encoded the EARS algorithms, deployed these algorithms using BioSTORM, and were able to reproduce and extend previously published evaluation results.

Conclusion

The validated model of aberrancy-detection algorithms and its software implementation will enable principled comparison of algorithms, synthesis of results from evaluation studies, and identification of surveillance algorithms for use in specific public health settings.     

Veterans' voices: use of the American Customer Satisfaction Index (ACSI) Survey to identify My HealtheVet personal health record users' characteristics,needs, and preferences

Background

Consumer research reveals considerable interest in the use of Personal Health Records (PHRs), yet adoption remains relatively low. Both adopters and nonadopters represent important perspectives from which to understand this paradox.

Objective

This study focuses on direct feedback from adopters obtained using the American Customer Satisfaction Index (ACSI) survey on the My HealtheVet PHR portal (http://www.myhealth.va.gov) of the Veterans Health Administration (VHA). The results represent a source of direct feedback with which to better understand veterans'' needs and preferences.

Methods

The ACSI Survey was implemented in October 2007 to measure satisfaction and elicit information about characteristics and preferences of My HealtheVet PHR adopters. The data represent a continuous random sample of site visitors who have navigated at least four pages on the site. A total of 100 617 surveys were completed (17.2%).

Results

Satisfaction with My HealtheVet is high (8.3/10.0), and users are highly likely to return to the site (8.6/10.0) and recommend the site to other veterans (9.1/10.0). The majority of system adopters are male (91%), between the ages of 51 and 70 (68%), and served in the Vietnam War (60%). Most veterans currently visit the site to utilize pharmacy-related features.

Conclusion

VHA has used the ACSI to monitor satisfaction, and to better understand the characteristics, needs, and preferences of early adopters. The data provide an important source of direct feedback to inform program development. Future research will include monitoring the impact of enhancements and new features on satisfaction, and conducting additional research with nonadopters to identify barriers to adoption and use.     

Research and applications: The discriminatory cost of ICD-10-CM transition between clinical specialties: metrics,case study,and mitigating tools

Objective

Applying the science of networks to quantify the discriminatory impact of the ICD-9-CM to ICD-10-CM transition between clinical specialties.

Materials and Methods

Datasets were the Center for Medicaid and Medicare Services ICD-9-CM to ICD-10-CM mapping files, general equivalence mappings, and statewide Medicaid emergency department billing. Diagnoses were represented as nodes and their mappings as directional relationships. The complex network was synthesized as an aggregate of simpler motifs and tabulation per clinical specialty.

Results

We identified five mapping motif categories: identity, class-to-subclass, subclass-to-class, convoluted, and no mapping. Convoluted mappings indicate that multiple ICD-9-CM and ICD-10-CM codes share complex, entangled, and non-reciprocal mappings. The proportions of convoluted diagnoses mappings (36% overall) range from 5% (hematology) to 60% (obstetrics and injuries). In a case study of 24 008 patient visits in 217 emergency departments, 27% of the costs are associated with convoluted diagnoses, with ‘abdominal pain’ and ‘gastroenteritis’ accounting for approximately 3.5%.

Discussion

Previous qualitative studies report that administrators and clinicians are likely to be challenged in understanding and managing their practice because of the ICD-10-CM transition. We substantiate the complexity of this transition with a thorough quantitative summary per clinical specialty, a case study, and the tools to apply this methodology easily to any clinical practice in the form of a web portal and analytic tables.

Conclusions

Post-transition, successful management of frequent diseases with convoluted mapping network patterns is critical. The http://lussierlab.org/transition-to-ICD10CM web portal provides insight in linking onerous diseases to the ICD-10 transition.     

Training Multidisciplinary Biomedical Informatics Students: Three Years of Experience

Objective

The European INFOBIOMED Network of Excellence ¹ recognized that a successful education program in biomedical informatics should include not only traditional teaching activities in the basic sciences but also the development of skills for working in multidisciplinary teams.

Design

A carefully developed 3-year training program for biomedical informatics students addressed these educational aspects through the following four activities: (1) an internet course database containing an overview of all Medical Informatics and BioInformatics courses, (2) a BioMedical Informatics Summer School, (3) a mobility program based on a ‘brokerage service’ which published demands and offers, including funding for research exchange projects, and (4) training challenges aimed at the development of multi-disciplinary skills.

Measurements

This paper focuses on experiences gained in the development of novel educational activities addressing work in multidisciplinary teams. The training challenges described here were evaluated by asking participants to fill out forms with Likert scale based questions. For the mobility program a needs assessment was carried out.

Results

The mobility program supported 20 exchanges which fostered new BMI research, resulted in a number of peer-reviewed publications and demonstrated the feasibility of this multidisciplinary BMI approach within the European Union. Students unanimously indicated that the training challenge experience had contributed to their understanding and appreciation of multidisciplinary teamwork.

Conclusion

The training activities undertaken in INFOBIOMED have contributed to a multi-disciplinary BMI approach. It is our hope that this work might provide an impetus for training efforts in Europe, and yield a new generation of biomedical informaticians.     

An i2b2-based,generalizable, open source,self-scaling chronic disease registry

Objective

Registries are a well-established mechanism for obtaining high quality, disease-specific data, but are often highly project-specific in their design, implementation, and policies for data use. In contrast to the conventional model of centralized data contribution, warehousing, and control, we design a self-scaling registry technology for collaborative data sharing, based upon the widely adopted Integrating Biology & the Bedside (i2b2) data warehousing framework and the Shared Health Research Information Network (SHRINE) peer-to-peer networking software.

Materials and methods

Focusing our design around creation of a scalable solution for collaboration within multi-site disease registries, we leverage the i2b2 and SHRINE open source software to create a modular, ontology-based, federated infrastructure that provides research investigators full ownership and access to their contributed data while supporting permissioned yet robust data sharing. We accomplish these objectives via web services supporting peer-group overlays, group-aware data aggregation, and administrative functions.

Results

The 56-site Childhood Arthritis & Rheumatology Research Alliance (CARRA) Registry and 3-site Harvard Inflammatory Bowel Diseases Longitudinal Data Repository now utilize i2b2 self-scaling registry technology (i2b2-SSR). This platform, extensible to federation of multiple projects within and between research networks, encompasses >6000 subjects at sites throughout the USA.

Discussion

We utilize the i2b2-SSR platform to minimize technical barriers to collaboration while enabling fine-grained control over data sharing.

Conclusions

The implementation of i2b2-SSR for the multi-site, multi-stakeholder CARRA Registry has established a digital infrastructure for community-driven research data sharing in pediatric rheumatology in the USA. We envision i2b2-SSR as a scalable, reusable solution facilitating interdisciplinary research across diseases.     

A rule based solution to co-reference resolution in clinical text

Objective

To build an effective co-reference resolution system tailored to the biomedical domain.

Methods

Experimental materials used in this study were provided by the 2011 i2b2 Natural Language Processing Challenge. The 2011 i2b2 challenge involves co-reference resolution in medical documents. Concept mentions have been annotated in clinical texts, and the mentions that co-refer in each document are linked by co-reference chains. Normally, there are two ways of constructing a system to automatically discoverco-referent links. One is to manually build rules forco-reference resolution; the other is to use machine learning systems to learn automatically from training datasets and then perform the resolution task on testing datasets.

Results

The existing co-reference resolution systems are able to find some of the co-referent links; our rule based system performs well, finding the majority of the co-referent links. Our system achieved 89.6% overall performance on multiple medical datasets.

Conclusions

Manually crafted rules based on observation of training data is a valid way to accomplish high performance in this co-reference resolution task for the critical biomedical domain.     

Catharanthus roseus: a natural source for the synthesis of silver nanoparticles

Objective

To develop a simple rapid procedure for bioreduction of silver nanoparticles (AgNPs) using aqueous leaves extracts of Catharanthus roseus (C. roseus).

Methods

Characterization were determined by using UV-Vis spectrophotometry, scanning electron microscopy (SEM), energy dispersive X-ray and X-ray diffraction.

Results

SEM showed the formation of silver nanoparticles with an average size of 67 nm to 48 nm. X-ray diffraction analysis showed that the particles were crystalline in nature with face centered cubic geometry.

Conclusions

C. roseus demonstrates strong potential for synthesis of silver nanoparticles by rapid reduction of silver ions (Ag⁺ to Ag⁰). This study provides evidence for developing large scale commercial production of value-added products for biomedical/nanotechnology-based industries.     

Research and applications: Syntactic parsing of clinical text: guideline and corpus development with handling ill-formed sentences

Objective

To develop, evaluate, and share: (1) syntactic parsing guidelines for clinical text, with a new approach to handling ill-formed sentences; and (2) a clinical Treebank annotated according to the guidelines. To document the process and findings for readers with similar interest.

Methods

Using random samples from a shared natural language processing challenge dataset, we developed a handbook of domain-customized syntactic parsing guidelines based on iterative annotation and adjudication between two institutions. Special considerations were incorporated into the guidelines for handling ill-formed sentences, which are common in clinical text. Intra- and inter-annotator agreement rates were used to evaluate consistency in following the guidelines. Quantitative and qualitative properties of the annotated Treebank, as well as its use to retrain a statistical parser, were reported.

Results

A supplement to the Penn Treebank II guidelines was developed for annotating clinical sentences. After three iterations of annotation and adjudication on 450 sentences, the annotators reached an F-measure agreement rate of 0.930 (while intra-annotator rate was 0.948) on a final independent set. A total of 1100 sentences from progress notes were annotated that demonstrated domain-specific linguistic features. A statistical parser retrained with combined general English (mainly news text) annotations and our annotations achieved an accuracy of 0.811 (higher than models trained purely with either general or clinical sentences alone). Both the guidelines and syntactic annotations are made available at https://sourceforge.net/projects/medicaltreebank.

Conclusions

We developed guidelines for parsing clinical text and annotated a corpus accordingly. The high intra- and inter-annotator agreement rates showed decent consistency in following the guidelines. The corpus was shown to be useful in retraining a statistical parser that achieved moderate accuracy.     

Patient-controlled sharing of medical imaging data across unaffiliated healthcare organizations

Background

Current image sharing is carried out by manual transportation of CDs by patients or organization-coordinated sharing networks. The former places a significant burden on patients and providers. The latter faces challenges to patient privacy.

Objective

To allow healthcare providers efficient access to medical imaging data acquired at other unaffiliated healthcare facilities while ensuring strong protection of patient privacy and minimizing burden on patients, providers, and the information technology infrastructure.

Methods

An image sharing framework is described that involves patients as an integral part of, and with full control of, the image sharing process. Central to this framework is the Patient Controlled Access-key REgistry (PCARE) which manages the access keys issued by image source facilities. When digitally signed by patients, the access keys are used by any requesting facility to retrieve the associated imaging data from the source facility. A centralized patient portal, called a PCARE patient control portal, allows patients to manage all the access keys in PCARE.

Results

A prototype of the PCARE framework has been developed by extending open-source technology. The results for feasibility, performance, and user assessments are encouraging and demonstrate the benefits of patient-controlled image sharing.

Discussion

The PCARE framework is effective in many important clinical cases of image sharing and can be used to integrate organization-coordinated sharing networks. The same framework can also be used to realize a longitudinal virtual electronic health record.

Conclusion

The PCARE framework allows prior imaging data to be shared among unaffiliated healthcare facilities while protecting patient privacy with minimal burden on patients, providers, and infrastructure. A prototype has been implemented to demonstrate the feasibility and benefits of this approach.