Translational integrity and continuity: personalized biomedical data integration

A review of harmonization methods for studying dietary patterns

Data harmonization is the process by which each of the variables from different research studies are standardized to similar units resulting in comparable datasets. These data may be integrated for more powerful and accurate examination and prediction of outcomes for use in the intelligent and smart electronic health software programs and systems. Prospective harmonization is performed when researchers create guidelines for gathering and managing the data before data collection begins. In contrast, retrospective harmonization is performed by pooling previously collected data from various studies using expert domain knowledge to identify and translate variables. In nutritional epidemiology, dietary data harmonization is often necessary to construct the nutrient and food databases necessary to answer complex research questions and develop effective public health policy. In this paper, we review methods for effective data harmonization, including developing a harmonization plan, which common standards already exist for harmonization, and defining variables needed to harmonize datasets. Currently, several large-scale studies maintain harmonized nutrient databases, especially in Europe, and steps have been proposed to inform the retrospective harmonization process. As an example, data harmonization methods are applied to several U.S longitudinal diet datasets. Based on our review, considerations for future dietary data harmonization include user agreements for sharing private data among participating studies, defining variables and data dictionaries that accurately map variables among studies, and the use of secure data storage servers to maintain privacy. These considerations establish necessary components of harmonized data for smart health applications which can promote healthier eating and provide greater insights into the effect of dietary patterns on health.

Big data management challenges in health research-a literature review

Big data management for information centralization (i.e. making data of interest findable) and integration (i.e. making related data connectable) in health research is a defining challenge in biomedical informatics. While essential to create a foundation for knowledge discovery, optimized solutions to deliver high-quality and easy-to-use information resources are not thoroughly explored. In this review, we identify the gaps between current data management approaches and the need for new capacity to manage big data generated in advanced health research. Focusing on these unmet needs and well-recognized problems, we introduce state-of-the-art concepts, approaches and technologies for data management from computing academia and industry to explore improvement solutions. We explain the potential and significance of these advances for biomedical informatics. In addition, we discuss specific issues that have a great impact on technical solutions for developing the next generation of digital products (tools and data) to facilitate the raw-data-to-knowledge process in health research.

Using insurance claims to predict and improve hospitalizations and biologics use in members with inflammatory bowel diseases

OBJECTIVE

Inflammatory Bowel Disease (IBD) is an inflammatory disorder of the gastrointestinal tract that can necessitate hospitalization and the use of expensive biologics. Models predicting these interventions may improve patient quality of life and reduce expenditures.

MATERIALS AND METHODS

We used insurance claims from 2011 to 2013 to predict IBD-related hospitalizations and the initiation of biologics. We derived and optimized our model from a 2011 training set of 7771 members, predicting their outcomes the following year. The best-performing model was then applied to a 2012 validation set of 7450 members to predict their outcomes in 2013.

RESULTS

Our models predicted both IBD-related hospitalizations and the initiation of biologics, with average positive predictive values of 17% and 11%, respectively - each a 200% improvement over chance. Further, when we used topic modeling to identify four member subpopulations, the positive predictive value of predicting hospitalization increased to 20%.

DISCUSSION

We show that our hospitalization model, in concert with a mildly-effective interventional treatment plan for members identified as high-risk, may both improve patient outcomes and reduce insurance expenditures.

CONCLUSION

The success of our approach provides a roadmap for how claims data can complement traditional medical decision making with personalized, data-driven predictive medicine.

The biorepository portal toolkit: an honest brokered, modular service oriented software tool set for biospecimen-driven translational research

BACKGROUND

High throughput molecular sequencing and increased biospecimen variety have introduced significant informatics challenges for research biorepository infrastructures. We applied a modular system integration approach to develop an operational biorepository management system. This method enables aggregation of the clinical, specimen and genomic data collected for biorepository resources.

METHODS

We introduce an electronic Honest Broker (eHB) and Biorepository Portal (BRP) open source project that, in tandem, allow for data integration while protecting patient privacy. This modular approach allows data and specimens to be associated with a biorepository subject at any time point asynchronously. This lowers the bar to develop new research projects based on scientific merit without institutional review for a proposal.

RESULTS

By facilitating the automated de-identification of specimen and associated clinical and genomic data we create a future proofed specimen set that can withstand new workflows and be connected to new associated information over time. Thus facilitating collaborative advanced genomic and tissue research.

CONCLUSIONS

As of Janurary of 2016 there are 23 unique protocols/patient cohorts being managed in the Biorepository Portal (BRP). There are over 4000 unique subject records in the electronic honest broker (eHB), over 30,000 specimens accessioned and 8 institutions participating in various biobanking activities using this tool kit. We specifically set out to build rich annotation of biospecimens with longitudinal clinical data; BRP/REDCap integration for multi-institutional repositories; EMR integration; further annotated specimens with genomic data specific to a domain; build application hooks for experiments at the specimen level integrated with analytic software; while protecting privacy per the Office of Civil Rights (OCR) and HIPAA.

Relational Network for Knowledge Discovery through Heterogeneous Biomedical and Clinical Features

Biomedical big data, as a whole, covers numerous features, while each dataset specifically delineates part of them. "Full feature spectrum" knowledge discovery across heterogeneous data sources remains a major challenge. We developed a method called bootstrapping for unified feature association measurement (BUFAM) for pairwise association analysis, and relational dependency network (RDN) modeling for global module detection on features across breast cancer cohorts. Discovered knowledge was cross-validated using data from Wake Forest Baptist Medical Center's electronic medical records and annotated with BioCarta signaling signatures. The clinical potential of the discovered modules was exhibited by stratifying patients for drug responses. A series of discovered associations provided new insights into breast cancer, such as the effects of patient's cultural background on preferences for surgical procedure. We also discovered two groups of highly associated features, the HER2 and the ER modules, each of which described how phenotypes were associated with molecular signatures, diagnostic features, and clinical decisions. The discovered "ER module", which was dominated by cancer immunity, was used as an example for patient stratification and prediction of drug responses to tamoxifen and chemotherapy. BUFAM-derived RDN modeling demonstrated unique ability to discover clinically meaningful and actionable knowledge across highly heterogeneous biomedical big data sets.

Resolving complex research data management issues in biomedical laboratories: Qualitative study of an industry-academia collaboration

This paper describes a distributed collaborative effort between industry and academia to systematize data management in an academic biomedical laboratory. Heterogeneous and voluminous nature of research data created in biomedical laboratories make information management difficult and research unproductive. One such collaborative effort was evaluated over a period of four years using data collection methods including ethnographic observations, semi-structured interviews, web-based surveys, progress reports, conference call summaries, and face-to-face group discussions. Data were analyzed using qualitative methods of data analysis to (1) characterize specific problems faced by biomedical researchers with traditional information management practices, (2) identify intervention areas to introduce a new research information management system called Labmatrix, and finally to (3) evaluate and delineate important general collaboration (intervention) characteristics that can optimize outcomes of an implementation process in biomedical laboratories. Results emphasize the importance of end user perseverance, human-centric interoperability evaluation, and demonstration of return on investment of effort and time of laboratory members and industry personnel for success of implementation process. In addition, there is an intrinsic learning component associated with the implementation process of an information management system. Technology transfer experience in a complex environment such as the biomedical laboratory can be eased with use of information systems that support human and cognitive interoperability. Such informatics features can also contribute to successful collaboration and hopefully to scientific productivity.

Towards an Oncology Database (ONCOD) Using a Warehousing Approach

Here we report a model data mart developed upon a warehousing system focusing on oncology data to explore optimized system architecture to support enhanced data integration and application capacity.

Translational bioinformatics in psychoneuroimmunology: methods and applications

Translational bioinformatics plays an indispensable role in transforming psychoneuroimmunology (PNI) into personalized medicine. It provides a powerful method to bridge the gaps between various knowledge domains in PNI and systems biology. Translational bioinformatics methods at various systems levels can facilitate pattern recognition, and expedite and validate the discovery of systemic biomarkers to allow their incorporation into clinical trials and outcome assessments. Analysis of the correlations between genotypes and phenotypes including the behavioral-based profiles will contribute to the transition from the disease-based medicine to human-centered medicine. Translational bioinformatics would also enable the establishment of predictive models for patient responses to diseases, vaccines, and drugs. In PNI research, the development of systems biology models such as those of the neurons would play a critical role. Methods based on data integration, data mining, and knowledge representation are essential elements in building health information systems such as electronic health records and computerized decision support systems. Data integration of genes, pathophysiology, and behaviors are needed for a broad range of PNI studies. Knowledge discovery approaches such as network-based systems biology methods are valuable in studying the cross-talks among pathways in various brain regions involved in disorders such as Alzheimer's disease.

Translational research in pediatrics: tissue sampling and biobanking

Translational research is expanding and has become a focus of National Research funding agencies, touted as the primary avenue to improve health care practice. The use of human tissues for research on disease etiology is a pillar of translational research, particularly with innovations in research technologies to investigate the building blocks of disease. In pediatrics, translational research using human tissues has been hindered by the many practical and ethical considerations associated with tissue procurement from children and also by a limited population base for study, by the increasing complexities in conducting clinical research, and by a lack of dedicated child-health research funding. Given these obstacles, pediatric translational research can be enhanced by developing strategic and efficient biobanks that will provide scientists with quality tissue specimens to render accurate and reproducible research results. Indeed, tissue sampling and biobanking within pediatric academic settings has potential to impact child health by promoting bidirectional interaction between clinicians and scientists, helping to maximize research productivity, and providing a competitive edge for attracting and maintaining high-quality personnel. The authors of this review outline key issues and practical solutions to optimize pediatric tissue sampling and biobanking for translational research, activities that will ultimately reduce the burden of childhood disease.

Translational informatics in personalized medicine

Personalized medicine is often associated and discussed in the context of advances from the human genome project. It is true that significant breakthroughs and advancement of deep sequencing and other analytical technologies have greatly expanded the pool of available biological data, but integrating this data into medically meaningful knowledge via translational informatics remains an area of opportunity that is far from being fully realized. Significant opportunities remain for informatics to drive progress towards wide use and utility of personalized medicine by focusing and exploitation of multimodal biomarkers, precompetitive data sharing and a balance between high-content data and rich phenotypic data, such as real observational medical outcomes.

Proteomic characterization of non-small cell lung cancer in a comprehensive translational thoracic oncology database

BACKGROUND

In recent years, there has been tremendous growth and interest in translational research, particularly in cancer biology. This area of study clearly establishes the connection between laboratory experimentation and practical human application. Though it is common for laboratory and clinical data regarding patient specimens to be maintained separately, the storage of such heterogeneous data in one database offers many benefits as it may facilitate more rapid accession of data and provide researchers access to greater numbers of tissue samples.

DESCRIPTION

The Thoracic Oncology Program Database Project was developed to serve as a repository for well-annotated cancer specimen, clinical, genomic, and proteomic data obtained from tumor tissue studies. The TOPDP is not merely a library-it is a dynamic tool that may be used for data mining and exploratory analysis. Using the example of non-small cell lung cancer cases within the database, this study will demonstrate how clinical data may be combined with proteomic analyses of patient tissue samples in determining the functional relevance of protein over and under expression in this disease. Clinical data for 1323 patients with non-small cell lung cancer has been captured to date. Proteomic studies have been performed on tissue samples from 105 of these patients. These tissues have been analyzed for the expression of 33 different protein biomarkers using tissue microarrays. The expression of 15 potential biomarkers was found to be significantly higher in tumor versus matched normal tissue. Proteins belonging to the receptor tyrosine kinase family were particularly likely to be over expressed in tumor tissues. There was no difference in protein expression across various histologies or stages of non-small cell lung cancer. Though not differentially expressed between tumor and non-tumor tissues, the over expression of the glucocorticoid receptor (GR) was associated improved overall survival. However, this finding is preliminary and warrants further investigation.

CONCLUSION

Though the database project is still under development, the application of such a database has the potential to enhance our understanding of cancer biology and will help researchers to identify targets to modify the course of thoracic malignancies.

Predictive and prognostic molecular markers for cancer medicine

Over the last 10 years there has been an explosion of information about the molecular biology of cancer. A challenge in oncology is to translate this information into advances in patient care. While there are well-formed routes for translating new molecular information into drug therapy, the routes for translating new information into sensitive and specific diagnostic, prognostic and predictive tests are still being developed. Similarly, the science of using tumor molecular profiles to select clinical trial participants or to optimize therapy for individual patients is still in its infancy. This review will summarize the current technologies for predicting treatment response and prognosis in cancer medicine, and outline what the future may hold. It will also highlight the potential importance of methods that can integrate molecular, histopathological and clinical information into a synergistic understanding of tumor progression. While these possibilities are without doubt exciting, significant challenges remain if we are to implement them with a strong evidence base in a widely available and cost-effective manner.

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.

Neuroscience Data Integration through Mediation: An (F)BIRN Case Study

We describe an application of the BIRN mediator to the integration of neuroscience experimental data sources. The BIRN mediator is a general purpose solution to the problem of providing integrated, semantically-consistent access to biomedical data from multiple, distributed, heterogeneous data sources. The system follows the mediation approach, where the data remains at the sources, providers maintain control of the data, and the integration system retrieves data from the sources in real-time in response to client queries. Our aim with this paper is to illustrate how domain-specific data integration applications can be developed quickly and in a principled way by using our general mediation technology. We describe in detail the integration of two leading, but radically different, experimental neuroscience sources, namely, the human imaging database, a relational database, and the eXtensible neuroimaging archive toolkit, an XML web services system. We discuss the steps, sources of complexity, effort, and time required to build such applications, as well as outline directions of ongoing and future research on biomedical data integration.

The DEDUCE Guided Query tool: providing simplified access to clinical data for research and quality improvement

In many healthcare organizations, comparative effectiveness research and quality improvement (QI) investigations are hampered by a lack of access to data created as a byproduct of patient care. Data collection often hinges upon either manual chart review or ad hoc requests to technical experts who support legacy clinical systems. In order to facilitate this needed capacity for data exploration at our institution (Duke University Health System), we have designed and deployed a robust Web application for cohort identification and data extraction--the Duke Enterprise Data Unified Content Explorer (DEDUCE). DEDUCE is envisioned as a simple, web-based environment that allows investigators access to administrative, financial, and clinical information generated during patient care. By using business intelligence tools to create a view into Duke Medicine's enterprise data warehouse, DEDUCE provides a Guided Query functionality using a wizard-like interface that lets users filter through millions of clinical records, explore aggregate reports, and, export extracts. Researchers and QI specialists can obtain detailed patient- and observation-level extracts without needing to understand structured query language or the underlying database model. Developers designing such tools must devote sufficient training and develop application safeguards to ensure that patient-centered clinical researchers understand when observation-level extracts should be used. This may mitigate the risk of data being misunderstood and consequently used in an improper fashion.

Assessing the quality of annotations in asthma gene expression experiments

Background

The amount of data deposited in the Gene Expression Omnibus (GEO) has expanded significantly. It is important to ensure that these data are properly annotated with clinical data and descriptions of experimental conditions so that they can be useful for future analysis. This study assesses the adequacy of documented asthma markers in GEO. Three objective measures (coverage, consistency and association) were used for evaluation of annotations contained in 17 asthma studies.

Results

There were 918 asthma samples with 20,640 annotated markers. Of these markers, only 10,419 had documented values (50% coverage). In one study carefully examined for consistency, there were discrepancies in drug name usage, with brand name and generic name used in different sections to refer to the same drug. Annotated markers showed adequate association with other relevant variables (i.e. the use of medication only when its corresponding disease state was present).

Conclusions

There is inadequate variable coverage within GEO and usage of terms lacks consistency. Association between relevant variables, however, was adequate.

Translational bioinformatics and systems biology approaches for personalized medicine

Systems biology and pharmacogenomics are emerging and promising fields that will provide a thorough understanding of diseases and enable personalized therapy. However, one of the most significant obstacles in the practice of personalized medicine is the translation of scientific discoveries into better therapeutic outcomes. Translational bioinformatics is a powerful method to bridge the gap between systems biology research and clinical practice. This goal can be achieved through providing integrative methods to enable predictive models for therapeutic responses. As a media between bench and bedside, translational bioinformatics has the mission to meet challenges in the development of personalized medicine. On the biomedical side, translational bioinformatics would enable the identification of biomarkers based on systemic analyses. It can improve the understanding of the correlations between genotypes and phenotypes. It would enable novel insights of interactions and interrelationships among different parts in a whole system. On the informatics side, methods based on data integration, data mining, and knowledge representation can provide decision support for both researchers and clinicians. Data integration is not only for better data access, but also for knowledge discovery. Decision support based on translational bioinformatics means better information and workflow management, efficient literature and resource retrieval, and communication improvement. These approaches are crucial for understanding diseases and applying personalized therapeutics at systems levels.