caGrid 1.0: An Enterprise Grid Infrastructure for Biomedical Research

Objective

To develop software infrastructure that will provide support for discovery, characterization, integrated access, and management of diverse and disparate collections of information sources, analysis methods, and applications in biomedical research.

Design

An enterprise Grid software infrastructure, called caGrid version 1.0 (caGrid 1.0), has been developed as the core Grid architecture of the NCI-sponsored cancer Biomedical Informatics Grid (caBIG^™) program. It is designed to support a wide range of use cases in basic, translational, and clinical research, including 1) discovery, 2) integrated and large-scale data analysis, and 3) coordinated study.

Measurements

The caGrid is built as a Grid software infrastructure and leverages Grid computing technologies and the Web Services Resource Framework standards. It provides a set of core services, toolkits for the development and deployment of new community provided services, and application programming interfaces for building client applications.

Results

The caGrid 1.0 was released to the caBIG community in December 2006. It is built on open source components and caGrid source code is publicly and freely available under a liberal open source license. The core software, associated tools, and documentation can be downloaded from the following URL: https://cabig.nci.nih.gov/workspaces/Architecture/caGrid.

Conclusions

While caGrid 1.0 is designed to address use cases in cancer research, the requirements associated with discovery, analysis and integration of large scale data, and coordinated studies are common in other biomedical fields. In this respect, caGrid 1.0 is the realization of a framework that can benefit the entire biomedical community.     

Issues in Biomedical Research Data Management and Analysis: Needs and Barriers

Objectives

A. Identify the current state of data management needs of academic biomedical researchers. B. Explore their anticipated data management and analysis needs. C. Identify barriers to addressing those needs.

Design

A multimodal needs analysis was conducted using a combination of an online survey and in-depth one-on-one semi-structured interviews. Subjects were recruited via an e-mail list representing a wide range of academic biomedical researchers in the Pacific Northwest.

Measurements

The results from 286 survey respondents were used to provide triangulation of the qualitative analysis of data gathered from 15 semi-structured in-depth interviews.

Results

Three major themes were identified: 1) there continues to be widespread use of basic general-purpose applications for core data management; 2) there is broad perceived need for additional support in managing and analyzing large datasets; and 3) the barriers to acquiring currently available tools are most commonly related to financial burdens on small labs and unmet expectations of institutional support.

Conclusion

Themes identified in this study suggest that at least some common data management needs will best be served by improving access to basic level tools such that researchers can solve their own problems. Additionally, institutions and informaticians should focus on three components: 1) facilitate and encourage the use of modern data exchange models and standards, enabling researchers to leverage a common layer of interoperability and analysis; 2) improve the ability of researchers to maintain provenance of data and models as they evolve over time though tools and the leveraging of standards; and 3) develop and support information management service cores that could assist in these previous components while providing researchers with unique data analysis and information design support within a spectrum of informatics capabilities.     

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.     

Finding potentially new multimorbidity patterns of psychiatric and somatic diseases: exploring the use of literature-based discovery in primary care research

Background

Multimorbidity, the co-occurrence of two or more chronic medical conditions within a single individual, is increasingly becoming part of daily care of general medical practice. Literature-based discovery may help to investigate the patterns of multimorbidity and to integrate medical knowledge for improving healthcare delivery for individuals with co-occurring chronic conditions.

Objective

To explore the usefulness of literature-based discovery in primary care research through the key-case of finding associations between psychiatric and somatic diseases relevant to general practice in a large biomedical literature database (Medline).

Methods

By using literature based discovery for matching disease profiles as vectors in a high-dimensional associative concept space, co-occurrences of a broad spectrum of chronic medical conditions were matched for their potential in biomedicine. An experimental setting was chosen in parallel with expert evaluations and expert meetings to assess performance and to generate targets for integrating literature-based discovery in multidisciplinary medical research of psychiatric and somatic disease associations.

Results

Through stepwise reductions a reference set of 21 945 disease combinations was generated, from which a set of 166 combinations between psychiatric and somatic diseases was selected and assessed by text mining and expert evaluation.

Conclusions

Literature-based discovery tools generate specific patterns of associations between psychiatric and somatic diseases: one subset was appraised as promising for further research; the other subset surprised the experts, leading to intricate discussions and further eliciting of frameworks of biomedical knowledge. These frameworks enable us to specify targets for further developing and integrating literature-based discovery in multidisciplinary research of general practice, psychology and psychiatry, and epidemiology.     

Bionimbus: a cloud for managing,analyzing and sharing large genomics datasets

Background

As large genomics and phenotypic datasets are becoming more common, it is increasingly difficult for most researchers to access, manage, and analyze them. One possible approach is to provide the research community with several petabyte-scale cloud-based computing platforms containing these data, along with tools and resources to analyze it.

Methods

Bionimbus is an open source cloud-computing platform that is based primarily upon OpenStack, which manages on-demand virtual machines that provide the required computational resources, and GlusterFS, which is a high-performance clustered file system. Bionimbus also includes Tukey, which is a portal, and associated middleware that provides a single entry point and a single sign on for the various Bionimbus resources; and Yates, which automates the installation, configuration, and maintenance of the software infrastructure required.

Results

Bionimbus is used by a variety of projects to process genomics and phenotypic data. For example, it is used by an acute myeloid leukemia resequencing project at the University of Chicago. The project requires several computational pipelines, including pipelines for quality control, alignment, variant calling, and annotation. For each sample, the alignment step requires eight CPUs for about 12 h. BAM file sizes ranged from 5 GB to 10 GB for each sample.

Conclusions

Most members of the research community have difficulty downloading large genomics datasets and obtaining sufficient storage and computer resources to manage and analyze the data. Cloud computing platforms, such as Bionimbus, with data commons that contain large genomics datasets, are one choice for broadening access to research data in genomics.     

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.     

Life sciences domain analysis model

Objective

Meaningful exchange of information is a fundamental challenge in collaborative biomedical research. To help address this, the authors developed the Life Sciences Domain Analysis Model (LS DAM), an information model that provides a framework for communication among domain experts and technical teams developing information systems to support biomedical research. The LS DAM is harmonized with the Biomedical Research Integrated Domain Group (BRIDG) model of protocol-driven clinical research. Together, these models can facilitate data exchange for translational research.

Materials and methods

The content of the LS DAM was driven by analysis of life sciences and translational research scenarios and the concepts in the model are derived from existing information models, reference models and data exchange formats. The model is represented in the Unified Modeling Language and uses ISO 21090 data types.

Results

The LS DAM v2.2.1 is comprised of 130 classes and covers several core areas including Experiment, Molecular Biology, Molecular Databases and Specimen. Nearly half of these classes originate from the BRIDG model, emphasizing the semantic harmonization between these models. Validation of the LS DAM against independently derived information models, research scenarios and reference databases supports its general applicability to represent life sciences research.

Discussion

The LS DAM provides unambiguous definitions for concepts required to describe life sciences research. The processes established to achieve consensus among domain experts will be applied in future iterations and may be broadly applicable to other standardization efforts.

Conclusions

The LS DAM provides common semantics for life sciences research. Through harmonization with BRIDG, it promotes interoperability in translational science.     

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.     

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.     

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.     

Design and development of an international clinical data exchange system: the international layer function of the Dolphin Project

Objective

At present, most clinical data are exchanged between organizations within a regional system. However, people traveling abroad may need to visit a hospital, which would make international exchange of clinical data very useful.

Background

Since 2007, a collaborative effort to achieve clinical data sharing has been carried out at Zhejiang University in China and Kyoto University and Miyazaki University in Japan; each is running a regional clinical information center.

Methods

An international layer system named Global Dolphin was constructed with several key services, sharing patients'' health information between countries using a medical markup language (MML). The system was piloted with 39 test patients.

Results

The three regions above have records for 966 000 unique patients, which are available through Global Dolphin. Data exchanged successfully from Japan to China for the 39 study patients include 1001 MML files and 152 images. The MML files contained 197 free text-type paragraphs that needed human translation.

Discussion

The pilot test in Global Dolphin demonstrates that patient information can be shared across countries through international health data exchange. To achieve cross-border sharing of clinical data, some key issues had to be addressed: establishment of a super directory service across countries; data transformation; and unique one—language translation. Privacy protection was also taken into account. The system is now ready for live use.

Conclusion

The project demonstrates a means of achieving worldwide accessibility of medical data, by which the integrity and continuity of patients'' health information can be maintained.     

Health Professionals Expose TB Patients to Stigmatization in Society: Insights from Communities in an Urban District in Ghana

Objectives

To determine how the activities and attitudes of health professionals expose TB patients to stigmatization in the community.

Design

Qualitative research approach using individual interviews and focus groups

Setting

Shama Ahanta East Metropolitan district in the western region of Ghana

Participants

Members in nine communities in the district

Outcome measures

Words and statements that depict how activities and attitudes of health professionals may expose TB patients to stigmatization

Results

Five interrelated ways by which activities and attitudes of health professionals may expose TB patients to stigmatization in the community were identified in data: TB control practices; fear-based responses to TB; inappropriate health education messages; medical licensing for sellers; and prohibition of burial rites.

Conclusions

The findings may explain the diagnostic delay and low TB case detection rate in Ghana. This calls for intensification of education on TB and regular organization of refresher courses and possibly retraining of health professionals in TB control and management. When health professionals are seen to be treating TB patients as ‘normal’ individuals, it has the potential of changing the society''s perception about the disease.     

Improving performance of natural language processing part-of-speech tagging on clinical narratives through domain adaptation

Objective

Natural language processing (NLP) tasks are commonly decomposed into subtasks, chained together to form processing pipelines. The residual error produced in these subtasks propagates, adversely affecting the end objectives. Limited availability of annotated clinical data remains a barrier to reaching state-of-the-art operating characteristics using statistically based NLP tools in the clinical domain. Here we explore the unique linguistic constructions of clinical texts and demonstrate the loss in operating characteristics when out-of-the-box part-of-speech (POS) tagging tools are applied to the clinical domain. We test a domain adaptation approach integrating a novel lexical-generation probability rule used in a transformation-based learner to boost POS performance on clinical narratives.

Methods

Two target corpora from independent healthcare institutions were constructed from high frequency clinical narratives. Four leading POS taggers with their out-of-the-box models trained from general English and biomedical abstracts were evaluated against these clinical corpora. A high performing domain adaptation method, Easy Adapt, was compared to our newly proposed method ClinAdapt.

Results

The evaluated POS taggers drop in accuracy by 8.5–15% when tested on clinical narratives. The highest performing tagger reports an accuracy of 88.6%. Domain adaptation with Easy Adapt reports accuracies of 88.3–91.0% on clinical texts. ClinAdapt reports 93.2–93.9%.

Conclusions

ClinAdapt successfully boosts POS tagging performance through domain adaptation requiring a modest amount of annotated clinical data. Improving the performance of critical NLP subtasks is expected to reduce pipeline error propagation leading to better overall results on complex processing tasks.     

An analysis of electronic health record-related patient safety concerns

Objective

A recent Institute of Medicine report called for attention to safety issues related to electronic health records (EHRs). We analyzed EHR-related safety concerns reported within a large, integrated healthcare system.

Methods

The Informatics Patient Safety Office of the Veterans Health Administration (VA) maintains a non-punitive, voluntary reporting system to collect and investigate EHR-related safety concerns (ie, adverse events, potential events, and near misses). We analyzed completed investigations using an eight-dimension sociotechnical conceptual model that accounted for both technical and non-technical dimensions of safety. Using the framework analysis approach to qualitative data, we identified emergent and recurring safety concerns common to multiple reports.

Results

We extracted 100 consecutive, unique, closed investigations between August 2009 and May 2013 from 344 reported incidents. Seventy-four involved unsafe technology and 25 involved unsafe use of technology. A majority (70%) involved two or more model dimensions. Most often, non-technical dimensions such as workflow, policies, and personnel interacted in a complex fashion with technical dimensions such as software/hardware, content, and user interface to produce safety concerns. Most (94%) safety concerns related to either unmet data-display needs in the EHR (ie, displayed information available to the end user failed to reduce uncertainty or led to increased potential for patient harm), software upgrades or modifications, data transmission between components of the EHR, or ‘hidden dependencies’ within the EHR.

Discussion

EHR-related safety concerns involving both unsafe technology and unsafe use of technology persist long after ‘go-live’ and despite the sophisticated EHR infrastructure represented in our data source. Currently, few healthcare institutions have reporting and analysis capabilities similar to the VA.

Conclusions

Because EHR-related safety concerns have complex sociotechnical origins, institutions with long-standing as well as recent EHR implementations should build a robust infrastructure to monitor and learn from them.     

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.     

Performance factors of mobile rich media job aids for community health workers

Objective

To study and analyze the possible benefits on performance of community health workers using point-of-care clinical guidelines implemented as interactive rich media job aids on small-format mobile platforms.

Design

A crossover study with one intervention (rich media job aids) and one control (traditional job aids), two periods, with 50 community health workers, each subject solving a total 15 standardized cases per period per period (30 cases in total per subject).

Measurements

Error rate per case and task, protocol compliance.

Results

A total of 1394 cases were evaluated. Intervention reduces errors by an average of 33.15% (p=0.001) and increases protocol compliance 30.18% (p<0.001).

Limitations

Medical cases were presented on human patient simulators in a laboratory setting, not on real patients.

Conclusion

These results indicate encouraging prospects for mHealth technologies in general, and the use of rich media clinical guidelines on cell phones in particular, for the improvement of community health worker performance in developing countries.     

Bi-directional semantic similarity for gene ontology to optimize biological and clinical analyses

Background

Semantic similarity analysis facilitates automated semantic explanations of biological and clinical data annotated by biomedical ontologies. Gene ontology (GO) has become one of the most important biomedical ontologies with a set of controlled vocabularies, providing rich semantic annotations for genes and molecular phenotypes for diseases. Current methods for measuring GO semantic similarities are limited to considering only the ancestor terms while neglecting the descendants. One can find many GO term pairs whose ancestors are identical but whose descendants are very different and vice versa. Moreover, the lower parts of GO trees are full of terms with more specific semantics.

Methods

This study proposed a method of measuring semantic similarities between GO terms using the entire GO tree structure, including both the upper (ancestral) and the lower (descendant) parts. Comprehensive comparison studies were performed with well-known information content-based and graph structure-based semantic similarity measures with protein sequence similarities, gene expression-profile correlations, protein–protein interactions, and biological pathway analyses.

Conclusion

The proposed bidirectional measure of semantic similarity outperformed other graph-based and information content-based methods.     

Adoption of Order Entry with Decision Support for Chronic Care by Physician Organizations

Objective

This study sought to explore physician organizations’ adoption of chronic care guidelines in order entry systems and to investigate the organizational and market-related factors associated with this adoption.

Design

A quantitative nationwide survey of all primary care medical groups in the United States with 20 or more physicians; data were collected on 1,104 physician organizations, representing a 70% response rate.

Measurements

Measurements were the presence of an asthma, diabetes, or congestive heart failure guideline in a physician organization’s order entry system; size; age of the organization; number of clinic locations; type of ownership; health maintenance organization market penetration; urban/rural location; and presence of external incentives to improve quality of care.

Results

Only 27% of organizations reported access to order entry with decision support for chronic disease care. External incentives for quality is the only factor significantly associated with adoption of these tools. Organizations experiencing greater external incentives for quality are more likely to adopt order entry with decision support.

Conclusion

Because external incentives are strong drivers of adoption, policies requiring reporting of chronic care measurements and rewarding improvement as well as financial incentives for use of specific information technology tools are likely to accelerate adoption of order entry with decision support.