Data libraries - the missing element for modeling biological systems.

The primary bottleneck in understanding and modeling biological systems is shifting from data collection to data analysis and integration. This process critically depends on data being available in an organized form, so that they can be accessed, understood, and reused by a broad community of scientists. A proven solution for organizing data is literature curation, which extracts, aggregates, and distributes findings from publications. Here, I describe the benefits of extending curation practices to datasets, especially those that are not deposited in centralized databases. I argue that dataset curation (or 'data librarianship' as I suggest we call it) will overcome many barriers in data visibility and reusability and make a unique contribution to integration and modeling.

Accuracy of an administrative database for pancreatic cancer by international classification of disease 10th codes: A retrospective large-cohort study.

BACKGROUND: Korean National Health Insurance (NHI) claims database provides large-cohort. However, studies regarding accuracy of administrative database for pancreatic cancer (PC) have not been reported. We aimed to identify accuracy of NHI database regarding PC classified by international classification of disease (ICD)-10 codes. AIM: To identify the accuracy and usefulness of administrative database in PC and the accurate ICD codes for PC with location. METHODS: Study and control groups were collected from 2003 to 2016 at Seoul National University Bundang Hospital. Cases of PC were identified in NHI database by international classification of diseases, 10th revision edition (ICD-10 codes) supported with V codes. V code is issued by medical doctors for covering 95% of medical cost by Korean government. According to pathologic reports, definite or possible diagnoses were defined using medical records, images, and pathology. RESULTS: A total of 1846 cases with PC and controls were collected. Among PC, only 410 (22.2%) cases were identified as specific cancer sites including head in 234 (12.7%) cases, tail in 104 (5.6%) cases and body in 72 (3.9%) cases. Among PC, 910 (49.3%) cases were diagnosed by definite criteria. Most of these were adenocarcinoma (98.0%). The rates of definite diagnosis of PC were highest in head (70.1%) followed by body (47.2%) and tail (43.3%). False-positive cases were pancreatic cystic neoplasm and metastasis to the pancreas. In terms of the overall diagnosis of PC, sensitivity, specificity, positive predictive value, and negative predictive value were 99.95%, 98.72%, 98.70%, and 99.95%, respectively. Diagnostic accuracy was similar both in terms of diagnostic criteria and tumor locations. CONCLUSION: Korean NHI claims database collected according to ICD-10 code with V code for PC showed good accuracy.

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.

Association Between BRAFV600E Mutation and the American College of Radiology Thyroid Imaging, Reporting and Data System in Solitary Papillary Thyroid Carcinoma.

RATIONALE AND OBJECTIVES: The purpose of this study was to evaluate the associations between BRAFV600E mutation, the American College of Radiology (ACR) thyroid imaging, reporting and data system (TI-RADS) on ultrasound and clinicopathological characteristics in patients with a solitary papillary thyroid carcinoma (PTC). MATERIALS AND METHODS: This retrospective study included 397 patients with a solitary PTC, proved pathologically. BRAFV600E mutation status was detected in postoperative samples by real-time fluorescent polymerase chain reaction. Associations of BRAFV600E mutation with the ACR TI-RADS and clinicopathological characteristics were analyzed. RESULTS: In this study, the incidence of BRAFV600E mutation was 81.4% (323/397) in patients with a solitary PTC. Univariate analyses showed that BRAFV600E mutation was significantly associated with margin, higher ACR TI-RADS point scores, and Hashimoto's thyroiditis. In multivariate analyses, lobulated or irregular margin was independently associated with BRAFV600E mutation in total solitary PTC. Furthermore, both in total solitary PTC and papillary thyroid microcarcinoma, BRAFV600E mutation was associated with ACR TI-RADS point scores, which was positively correlated with the risk of BRAFV600E mutation. There was no significant relationship between BRAFV600E mutation and ACR TI-RADS point scores in PTC >10 mm. In addition, Hashimoto's thyroiditis had a significant negative association with BRAFV600E mutation. CONCLUSION: A lobulated or irregular margin of the thyroid nodule is independently associated with BRAFV600E mutation in patients with PTC. In addition, higher ACR TI-RADS point scores is an independent risk factor for BRAFV600E mutation, and ACR TI-RADS point scores is positively associated with the risk of BRAFV600E mutation in solitary PTC, especially in papillary thyroid microcarcinoma. Our findings may be helpful for preoperative identification and medical management of PTC patients with BRAFV600E mutation.

Centralized Clinical Trial Imaging Data Management: Practical Guidance from a Comprehensive Cancer Center's Experience.

Medical imaging is an integral part of clinical trial research and it must be managed properly to provide accurate data to the sponsor in a timely manner (Clune in Cancer Inform 4:33-56, 2007; Wang et al. in Proc SPIE Int Soc Opt Eng 7967, 2011). Standardized workflows for site qualification, protocol preparation, data storage, retrieval, de-identification, submission, and query resolution are paramount to achieve quality clinical trial data management such as reducing the number of imaging protocol deviations and avoiding delays in data transfer. Centralization of data management and implementation of relational databases and electronic workflows can help maintain consistency and accuracy of imaging data. This technical note aims at sharing the practical implementation of our centralized clinical trial imaging data management processes to avoid the fragmentation of tasks among various disease centers and research staff, and enable us to provide quality, accurate, and timely imaging data to clinical trial sponsors.

qPortal: A platform for data-driven biomedical research.

Modern biomedical research aims at drawing biological conclusions from large, highly complex biological datasets. It has become common practice to make extensive use of high-throughput technologies that produce big amounts of heterogeneous data. In addition to the ever-improving accuracy, methods are getting faster and cheaper, resulting in a steadily increasing need for scalable data management and easily accessible means of analysis. We present qPortal, a platform providing users with an intuitive way to manage and analyze quantitative biological data. The backend leverages a variety of concepts and technologies, such as relational databases, data stores, data models and means of data transfer, as well as front-end solutions to give users access to data management and easy-to-use analysis options. Users are empowered to conduct their experiments from the experimental design to the visualization of their results through the platform. Here, we illustrate the feature-rich portal by simulating a biomedical study based on publically available data. We demonstrate the software's strength in supporting the entire project life cycle. The software supports the project design and registration, empowers users to do all-digital project management and finally provides means to perform analysis. We compare our approach to Galaxy, one of the most widely used scientific workflow and analysis platforms in computational biology. Application of both systems to a small case study shows the differences between a data-driven approach (qPortal) and a workflow-driven approach (Galaxy). qPortal, a one-stop-shop solution for biomedical projects offers up-to-date analysis pipelines, quality control workflows, and visualization tools. Through intensive user interactions, appropriate data models have been developed. These models build the foundation of our biological data management system and provide possibilities to annotate data, query metadata for statistics and future re-analysis on high-performance computing systems via coupling of workflow management systems. Integration of project and data management as well as workflow resources in one place present clear advantages over existing solutions.

Linked Registries: Connecting Rare Diseases Patient Registries through a Semantic Web Layer.

Patient registries are an essential tool to increase current knowledge regarding rare diseases. Understanding these data is a vital step to improve patient treatments and to create the most adequate tools for personalized medicine. However, the growing number of disease-specific patient registries brings also new technical challenges. Usually, these systems are developed as closed data silos, with independent formats and models, lacking comprehensive mechanisms to enable data sharing. To tackle these challenges, we developed a Semantic Web based solution that allows connecting distributed and heterogeneous registries, enabling the federation of knowledge between multiple independent environments. This semantic layer creates a holistic view over a set of anonymised registries, supporting semantic data representation, integrated access, and querying. The implemented system gave us the opportunity to answer challenging questions across disperse rare disease patient registries. The interconnection between those registries using Semantic Web technologies benefits our final solution in a way that we can query single or multiple instances according to our needs. The outcome is a unique semantic layer, connecting miscellaneous registries and delivering a lightweight holistic perspective over the wealth of knowledge stemming from linked rare disease patient registries.

Surrogate for oropharyngeal cancer HPV status in cancer database studies.

BACKGROUND: The utility of cancer databases for oropharyngeal cancer studies is limited by lack of information on human papillomavirus (HPV) status. The purpose of this study was to develop a surrogate that can be used to adjust for the effect of HPV status on survival. METHODS: The study cohort included 6419 patients diagnosed with oropharyngeal squamous cell carcinoma between 2004 and 2012, identified in the National Cancer Database (NCDB). The HPV surrogate score was developed using a logistic regression model predicting HPV-positive status. RESULTS: The HPV surrogate score was predictive of HPV status (area under the curve [AUC] 0.73; accuracy of 70.4%). Similar to HPV-positive tumors, HPV surrogate positive tumors were associated with improved overall survival (OS; hazard ratio [HR] 0.73; 95% confidence interval [CI] 0.59-0.91; P = .005), after adjusting for important covariates. CONCLUSION: The HPV surrogate score is useful for adjusting for the effect of HPV status on survival in studies utilizing cancer databases.

Clinical Data Warehouse: An Effective Tool to Create Intelligence in Disease Management.

Clinical business intelligence tools such as clinical data warehouse enable health care organizations to objectively assess the disease management programs that affect the quality of patients' life and well-being in public. The purpose of these programs is to reduce disease occurrence, improve patient care, and decrease health care costs. Therefore, applying clinical data warehouse can be effective in generating useful information about aspects of patient care to facilitate budgeting, planning, research, process improvement, external reporting, benchmarking, and trend analysis, as well as to enable the decisions needed to prevent the progression or appearance of the illness aligning with maintaining the health of the population. The aim of this review article is to describe the benefits of clinical data warehouse applications in creating intelligence for disease management programs.

Needles in the haystack: Using open-text fields to identify persons with intellectual and developmental disabilities in administrative home care data.

BACKGROUND: Use of administrative health data to study populations of interest is becoming more common. Identifying individuals with intellectual and developmental disabilities (IDD) in existing databases can be challenging due to inconsistent definitions and terminologies of IDD over time and across sectors, and the inability to rely on etiologies of IDD as they are frequently unknown. AIMS: To identify diagnoses related to IDD in an administrative database and create a cohort of persons with IDD. METHODS: Open-text diagnostic entries related to IDD were identified in an Ontario home care database (2003-2015) and coded as being either acceptable (e.g. Down syndrome) or ambiguous (e.g. intellectually challenged). The cognitive and functional skills of the resulting groups were compared using logistic regressions and standardized differences, and their age distributions were compared to that of the general home care population. RESULTS: Just under 1% of the home care population had a diagnostic entry related to IDD. Ambiguous terms were most commonly used (61%), and this group tended to be older and less impaired than the group with more acceptable terms used to describe their IDD. CONCLUSIONS: Open-text diagnostic variables in administrative health records can be used to identify and study individuals with IDD. IMPLICATIONS: Future work is needed to educate assessors on the importance of using standard, accepted terminology when recording diagnoses related to IDD.

A comparative effectiveness study of eSource used for data capture for a clinical research registry.

OBJECTIVE: This pilot study compared eSource-enabled versus traditional manual data transcription (non-eSource methods) for the collection of clinical registry information. The primary study objective was to compare the time spent completing registry forms using eSource versus non-eSource methods The secondary objectives were to compare data quality associated with these two data capture methods and the flexibility of the workflows. This study directly addressed fundamental questions relating to eSource adoption: what time-savings can be realized, and to what extent does eSource improve data quality. MATERIALS AND METHODS: The study used time and motion methods to compare eSource versus non-eSource data capture workflows for a single center OB/GYN registry. Direct observation by industrial engineers using specialized computer software captured keystrokes, mouse clicks and video recordings of the study team in their normal work environment completing real-time data collection. RESULTS: The overall average data capture time was reduced with eSource versus non-eSource methods (difference, 151s per case; eSource, 1603s; non-eSource, 1754s; p=0.051). The average data capture time for the demographic data was reduced (difference, 79s per case; eSource, 133s; non-eSource, 213s; p<0.001). This represents a 37% time reduction (95% confidence interval 27% to 47%). eSourced data field transcription errors were also reduced (eSource, 0%; non-eSource, 9%). CONCLUSION: The use of eSource versus traditional data transcription was associated with a significant reduction in data entry time and data quality errors. Further studies in other settings are needed to validate these results.

The Georges Pompidou University Hospital Clinical Data Warehouse: A 8-years follow-up experience.

BACKGROUND: When developed jointly with clinical information systems, clinical data warehouses (CDWs) facilitate the reuse of healthcare data and leverage clinical research. OBJECTIVE: To describe both data access and use for clinical research, epidemiology and health service research of the "Hôpital Européen Georges Pompidou" (HEGP) CDW. METHODS: The CDW has been developed since 2008 using an i2b2 platform. It was made available to health professionals and researchers in October 2010. Procedures to access data have been implemented and different access levels have been distinguished according to the nature of queries. RESULTS: As of July 2016, the CDW contained the consolidated data of over 860,000 patients followed since the opening of the HEGP hospital in July 2000. These data correspond to more than 122 million clinical item values, 124 million biological item values, and 3.7 million free text reports. The ethics committee of the hospital evaluates all CDW projects that generate secondary data marts. Characteristics of the 74 research projects validated between January 2011 and December 2015 are described. CONCLUSION: The use of HEGP CDWs is a key facilitator for clinical research studies. It required however important methodological and organizational support efforts from a biomedical informatics department.

LIMS and Clinical Data Management.

In order to achieve more accurate disease prevention, diagnosis, and treatment, clinical and genetic data need extensive and systematically associated study. As one way to achieve precision medicine, a laboratory information management system (LIMS) can effectively associate clinical data in a macrocosmic aspect and genomic data in a microcosmic aspect. This chapter summarizes the application of the LIMS in a clinical data management and implementation mode. It also discusses the principles of a LIMS in clinical data management, as well as the opportunities and challenges in the context of medical informatics.

The Alzheimer's Disease Neuroimaging Initiative informatics core: A decade in review.

The Informatics Core of the Alzheimer's Disease Neuroimaging Initiative has coordinated data integration and dissemination for a continually growing and complex data set in which both data contributors and recipients span institutions, scientific disciplines, and geographic boundaries. This article provides an update on the accomplishments and future plans.

How I do it: a practical database management system to assist clinical research teams with data collection, organization, and reporting.

RATIONALE AND OBJECTIVES: The objective of this study was to demonstrate that an intra-arterial liver therapy clinical research database system is a more workflow efficient and robust tool for clinical research than a spreadsheet storage system. The database system could be used to generate clinical research study populations easily with custom search and retrieval criteria. MATERIALS AND METHODS: A questionnaire was designed and distributed to 21 board-certified radiologists to assess current data storage problems and clinician reception to a database management system. Based on the questionnaire findings, a customized database and user interface system were created to perform automatic calculations of clinical scores including staging systems such as the Child-Pugh and Barcelona Clinic Liver Cancer, and facilitates data input and output. RESULTS: Questionnaire participants were favorable to a database system. The interface retrieved study-relevant data accurately and effectively. The database effectively produced easy-to-read study-specific patient populations with custom-defined inclusion/exclusion criteria. CONCLUSIONS: The database management system is workflow efficient and robust in retrieving, storing, and analyzing data.

Enabling semantic interoperability in multi-centric clinical trials on breast cancer.

BACKGROUND AND OBJECTIVES: Post-genomic clinical trials require the participation of multiple institutions, and collecting data from several hospitals, laboratories and research facilities. This paper presents a standard-based solution to provide a uniform access endpoint to patient data involved in current clinical research. METHODS: The proposed approach exploits well-established standards such as HL7 v3 or SPARQL and medical vocabularies such as SNOMED CT, LOINC and HGNC. A novel mechanism to exploit semantic normalization among HL7-based data models and biomedical ontologies has been created by using Semantic Web technologies. RESULTS: Different types of queries have been used for testing the semantic interoperability solution described in this paper. The execution times obtained in the tests enable the development of end user tools within a framework that requires efficient retrieval of integrated data. CONCLUSIONS: The proposed approach has been successfully tested by applications within the INTEGRATE and EURECA EU projects. These applications have been deployed and tested for: (i) patient screening, (ii) trial recruitment, and (iii) retrospective analysis; exploiting semantically interoperable access to clinical patient data from heterogeneous data sources.

A substitution method to improve completeness of events documentation in anesthesia records.

AIMS are optimized to find and display data and curves about one specific intervention but is not retrospective analysis on a huge volume of interventions. Such a system present two main limitation; (1) the transactional database architecture, (2) the completeness of documentation. In order to solve the architectural problem, data warehouses were developed to propose architecture suitable for analysis. However, completeness of documentation stays unsolved. In this paper, we describe a method which allows determining of substitution rules in order to detect missing anesthesia events in an anesthesia record. Our method is based on the principle that missing event could be detected using a substitution one defined as the nearest documented event. As an example, we focused on the automatic detection of the start and the end of anesthesia procedure when these events were not documented by the clinicians. We applied our method on a set of records in order to evaluate; (1) the event detection accuracy, (2) the improvement of valid records. For the year 2010-2012, we obtained event detection with a precision of 0.00 (-2.22; 2.00) min for the start of anesthesia and 0.10 (0.00; 0.35) min for the end of anesthesia. On the other hand, we increased by 21.1% the data completeness (from 80.3 to 97.2% of the total database) for the start and the end of anesthesia events. This method seems to be efficient to replace missing "start and end of anesthesia" events. This method could also be used to replace other missing time events in this particular data warehouse as well as in other kind of data warehouses.

[Construction and thinking of data element standard directory of traditional Chinese medicine clinical pharmacy information].

The aim of this study was to develop the data element standard directory of traditional Chinese medicine (TCM) clinical pharmacy information, to provide application standards and models of TCM clinical pharmacy for the electronic medical record (EMR). The developed line of work is as follows: initially establish research through four forms: literature analysis, questionnaires, discussion groups, expert advice. The research range from the Chinese herbal medicine research, herbal origin, harvesting, processing, identification of traits, physical and chemical identification, modern research, character, taste, Indications, clinical application, processing, dispensing medicine, Chinese medicine specifications, usage, dosage, caution, efficacy indications to small packaging applications, drug research, management and other related issues, including traditional Chinese medicine theory, application and hospital management information; according to the general and part 16 content of the national "Health Information Data Element Standards", and the basic method of extracting data element to study and develop the data element of TCM clinical pharmacy information from the defining content. Correspondingly propose the ideas and methods of construction of the "Data Element Standard Directory of TCM Clinical Pharmacy Information", sort out medicine clinical information data element standard catalog, divided into basic categories, clinical application class, management class three parts, and set norms and standards of identifying data elements, definitions, allowable value of traditional Chinese medicine clinical information, and discuss the sources and standards of information collection, leaving the interface, standardized and scientific terminology, docking with the existing standards, maintenance and management program and oter issues.

The value of all-payer claims databases to states.

All-payer claims databases are being developed in states across the nation to fill gaps in information about the health care system. The value of such databases is becoming more apparent as these databases mature and are used more frequently to help states better understand their health care utilization and costs.