Quality evaluation of value sets from cancer study common data elements using the UMLS semantic groups

The huge amounts of biomedical data are stored in various formats and accessed through numerous interfaces. It is a crucial task for Data integration and exchange in cancer research. Data elements play an important role in data integration. The NCI supports a broad initiative to standardize the common data elements (CDEs) used in cancer research data capture and reporting. The Taiwan Cancer Registry (TCR), established in 1979, is organized and funded by the Health Department of the central government. The TCR's primary goal is to survey the incidence of cancer in Taiwan. The aim of the Taiwan Cancer Common Data Element Project (TCCDEP) is to facilitate convergence towards a common metadata standard in Taiwan cancer registry data. The project is implemented using a set of open source software and tools developed by the NCI, such as the caCORE SDK and caGrid. The experience of building, learning and using the open toolkit, Cancer Data Standards Repository (caDSR), developed by the National Cancer Institute's Center for Bioinformatics, NCICB, in the USA is reported. The caDSR is a metadata repository including CDEs used by NCI-sponsored organizations. The object of this work is to develop a database of metadata for medical data elements, referred to as the TCCDEP and to establish a common classification of data elements used in cancer registry. In this manuscript, we will be developing the common data elements using vocabulary standards, ontology and semantic modeling methodology. The CDEs included demographic data, clinical history, pathology data, and clinical outcome data including treatment, recurrence and vital status. These CDEs will be further enhanced to data sets across the participating cancer institutes to facilitate and supplement translational research. The Taiwan Cancer Registry (TCR) model and standard will be use as the basis for an electronic data standard repository to metadata or data descriptors. The TCCDEP developed 40 data elements to annotate the cancer registry data collected. In this project, we describe the process required to develop the model, the caDSR CDEs, and the results of the modeling effort. We address difficulties we encountered and modifications for solution. The caBIG (TM) grid project, gird model of Taiwan Cancer Registry (girdTCR), using the caCORE tools to define data elements for cancer registry has been shown to caBIG(TM)UML model project. Currently, the Taiwan cancer registry CDEs are released and available in CDE browser for reusing. Furthermore, we will extend our CDEs to daily clinical practice and trials, along with how the methods were used to fully implemented in hospitals and cancer research centers in Taiwan.

Several years ago, the National Cancer Institute (NCI) embarked on an effort to standardize data collection methods across the cooperative trials groups it funds. These data collection methods included common toxicity criteria (CTC) and common data elements (CDEs). To date, CDEs have been developed for phase III therapeutic trials related to breast, prostate, lung, and colon cancer. Recently, the NCI funded the American College of Radiology Imaging Network (ACRIN, http://www.acrin.org), a cooperative group dedicated to imaging trials. Simultaneously, new imaging tests have shown promise in screening for lung cancer. Thus, several groups within the NCI became keenly interested in developing CDEs for imaging trials. What follows is a description of the development of CDEs for chest CT screening for lung cancer that was undertaken in the last half of 1999.

The National Cancer Institute (NCI) has developed the Common Data Elements (CDE) to serve as a controlled vocabulary of data descriptors for cancer research, to facilitate data interchange and inter-operability between cancer research centers. We evaluated CDE's structure to see whether it could represent the elements necessary to support its intended purpose, and whether it could prevent errors and inconsistencies from being accidentally introduced. We also performed automated checks for certain types of content errors that provided a rough measure of curation quality. Evaluation was performed on CDE content downloaded via the NCI's CDE Browser, and transformed into relational database form. Evaluation was performed under three categories: 1) compatibility with the ISO/IEC 11179 metadata model, on which CDE structure is based, 2) features necessary for controlled vocabulary support, and 3) support for a stated NCI goal, set up of data collection forms for cancer research. Various limitations were identified both with respect to content (inconsistency, insufficient definition of elements, redundancy) as well as structure--particularly the need for term and relationship support, as well as the need for metadata supporting the explicit representation of electronic forms that utilize sets of common data elements. While there are numerous positive aspects to the CDE effort, there is considerable opportunity for improvement. Our recommendations include review of existing content by diverse experts in the cancer community; integration with the NCI thesaurus to take advantage of the latter's links to nationally used controlled vocabularies, and various schema enhancements required for electronic form support.

Biomedical terminologies have been increasingly used in modern biomedical research and applications to facilitate data management and ensure semantic interoperability. As part of the evolution process, new concepts are regularly added to biomedical terminologies in response to the evolving domain knowledge and emerging applications. Most existing concept enrichment methods suggest new concepts via directly importing knowledge from external sources. In this paper, we introduced a lexical method based on formal concept analysis (FCA) to identify potentially missing concepts in a given terminology by leveraging its intrinsic knowledge - concept names. We first construct the FCA formal context based on the lexical features of concepts. Then we perform multistage intersection to formalize new concepts and detect potentially missing concepts. We applied our method to the Disease or Disorder sub-hierarchy in the National Cancer Institute (NCI) Thesaurus (19.08d version) and identified a total of 8,983 potentially missing concepts. As a preliminary evaluation of our method to validate the potentially missing concepts, we further checked whether they were included in any external source terminology in the Unified Medical Language System (UMLS). The result showed that 592 out of 8,937 potentially missing concepts were found in the UMLS.

Lack of homogeneous definitions for imaging data and consensus on their relevance in the setting of subarachnoid hemorrhage and unruptured intracranial aneurysms lead to a difficulty of data pooling and lack of robust data. The aim of the National Institute of Health/National Institute of Neurological Disorders and Stroke, Unruptured Intracranial Aneurysm (UIA) and Subarachnoid Hemorrhage (SAH) Common Data Elements (CDE) Project was to standardize data elements to ultimately facilitate data pooling and establish a more robust data quality in future neurovascular research on UIA and SAH. For the subcommittee 'Radiological imaging of SAH,' international cerebrovascular specialists with imaging expertise in the setting of SAH were selected by the steering committee. CDEs were developed after reviewing the literature on neuroradiology and already existing CDEs for other neurological diseases. For prioritization, the CDEs were classified into 'Core,' 'Supplemental-Highly Recommended,' 'Supplemental' and 'Exploratory.' The subcommittee compiled 136 CDEs, 100 out of which were derived from previously established CDEs on ischemic stroke and 36 were newly created. The CDEs were assigned to four main categories (several CDEs were assigned to more than one category): 'Parenchymal imaging' with 42 CDEs, 'Angiography' with 49 CDEs, 'Perfusion imaging' with 20 CDEs, and 'Transcranial doppler' with 55 CDEs. The CDEs were classified into core, supplemental highly recommended, supplemental and exploratory elements. The core CDEs were imaging modality, imaging modality type, imaging modality vessel, angiography type, vessel angiography arterial anatomic site and imaging vessel angiography arterial result. The CDEs were established based on the current literature and consensus across cerebrovascular specialists. The use of these CDEs will facilitate standardization and aggregation of imaging data in the setting of SAH. However, the CDEs may require reevaluation and periodic adjustment based on current research and improved imaging quality and novel modalities.

In the domain of biomedical and health informatics, ontologies are widely used to capture knowledge ranging from bioinformatics such as gene, protein, protein interactions, etc. to clinical/healthcare informatics knowledge such as diseases, symptoms, treatment, medication, etc. Currently, one medical knowledge source that encapsulates a broad spectrum of medical knowledge is the Unified Medical Language System (UMLS), which can be defined as a compendium of diverse medical ontological standards. The primary components of the UMLS are: Semantic Network (UMLS-SN) – designed by interconnecting well-defined semantic types with semantic relationships, and Metathesaurus (UMLS-META) – the base of UMLS system that is comprised of millions of medical concepts from diverse medical standards. However, within the biomedical and health informatics community, the concepts of software engineering and domain modeling (using meta-models such as ERD, UML, and XML) are very successful in designing and implementing biomedical/health domain application models. In the current status, the UMLS-SN is primarily employed for classification of medical concepts in UMLS-META, but UMLS-SN knowledge can't be viewed or employed as a modeling framework for designing ontological models and is restricted to the UMLS environment. Thus, the impact of the biomedical semantics captured by UMLS-SN might be minimal in medical facilities, research and healthcare organizations that are highly influenced by software engineering, meta-models and domain model-based practices. In order fill this gap, the authors propose a meta-modeling framework for UMLS-SN based on the UML Profile (built using UML meta-model) that will result in a customized domain specific meta-model. This specialized meta-model that encapsulates the medical knowledge semantics of UMLS-SN can then be employed for designing ontological models or relevant healthcare application models and simultaneously be coherent with software meta-models and domain modeling practices.

BackgroundThe Cooperative Prostate Cancer Tissue Resource (CPCTR) is a consortium of four geographically dispersed institutions that are funded by the U.S. National Cancer Institute (NCI) to provide clinically annotated prostate cancer tissue samples to researchers. To facilitate this effort, it was critical to arrive at agreed upon common data elements (CDEs) that could be used to collect demographic, pathologic, treatment and clinical outcome data.MethodsThe CPCTR investigators convened a CDE curation subcommittee to develop and implement CDEs for the annotation of collected prostate tissues. The draft CDEs were refined and progressively annotated to make them ISO 11179 compliant. The CDEs were implemented in the CPCTR database and tested using software query tools developed by the investigators.ResultsBy collaborative consensus the CPCTR CDE subcommittee developed 145 data elements to annotate the tissue samples collected. These included for each case: 1) demographic data, 2) clinical history, 3) pathology specimen level elements to describe the staging, grading and other characteristics of individual surgical pathology cases, 4) tissue block level annotation critical to managing a virtual inventory of cases and facilitating case selection, and 5) clinical outcome data including treatment, recurrence and vital status. These elements have been used successfully to respond to over 60 requests by end-users for tissue, including paraffin blocks from cases with 5 to 10 years of follow up, tissue microarrays (TMAs), as well as frozen tissue collected prospectively for genomic profiling and genetic studies. The CPCTR CDEs have been fully implemented in two major tissue banks and have been shared with dozens of other tissue banking efforts.ConclusionThe freely available CDEs developed by the CPCTR are robust, based on "best practices" for tissue resources, and are ISO 11179 compliant. The process for CDE development described in this manuscript provides a framework model for other organ sites and has been used as a model for breast and melanoma tissue banking efforts.

The Unified Medical Language System (UMLS) integrates many well-established biomedical terminologies. The UMLS's semantic network (SN) can help orient users to the vast knowledge content of the Metathesaurus via its abstract conceptual view. However, the SN itself is large and complex, and may still be difficult to comprehend. Our technique partitions the SN into even higher-level abstract entities which are amenable to display on limited-sized computer screens. The basis for the partitioning is the distribution of the relationships within the SN. The results of applying our technique to the UMLS are discussed.

BackgroundUniversities are increasingly implementing programs to effectively respond to and manage sport-related concussions (SRCs). One such effort is to develop common data elements (CDEs) and standardize data collection methods. The objectives of this study were to describe CDEs currently collected by Big Ten and Ivy League universities for SRC studies, and to compare the data collected with the core CDEs recommended by the National Institute of Neurological Disorders and Stroke (NINDS).MethodsWe conducted an anonymous cross-sectional online survey among medical staff at the 14 Big Ten and 8 Ivy League universities (one per university) between September and October 2015. The survey instrument, including 9 questions corresponding to the concussion data collected before, during, and after a concussion, was developed and pilot-tested before field use. We analyzed patterns of the concussion CDEs being collected, including when, what, and how the data were collected and stored, and compared them with the NINDS' recommended core CDEs.ResultsA total of 19 out of 22 universities were included, with 13 from Big Ten and 6 from Ivy-League universities. All 19 participating universities currently collected concussion data with athletes before, during, and after a concussion. Great similarities in data collection were observed at baseline and acutely post-concussion across participating universities. All 19 universities collected at least one of the ten recommended acute symptoms checklists, and 18 universities collected one of the four recommended core neuropsychological function cognitive measures. However, CDEs in the sub-acute and chronic timeframes were limited, with only 9 (47%) universities collecting post-concussion short to long term outcome data. While over 60% of universities collected and stored concussion data electronically, only 17% to 42% of data collected were readily available for research.ConclusionsSignificant inter-institutional similarities in acute concussion CDEs were found. Further efforts should focus on collecting sub-acute and chronic timeframe core CDEs and creating data access protocols to facilitate evidence-based concussion prevention and treatment for all collegiate athletes.

Extending the tissue microarray data exchange specification for inclusion of data analysis results

The National Cancer Institute (NCI) Thesaurus (NCIt) is a biomedical ontology which has been developed for over a decade. Nearly every month from 2003 through 2011, the NCI has published an updated version of the NCIt to the Web as an OWL ontology (as well as in other formats). We collected all 88 OWL versions of the NCIt available and conducted a cross-sectional study on this corpus to investigate and characterize the evolution of the NCIt. In particular, we gathered and analysed various axiom and entity statistics, and carried out a reasoner performance test over the corpus. Additionally, we extracted two complete sets of pairwise, consecutive diffs: the first set was generated by a purely syntactic difference analysis (based on OWL's notion of “structural equivalence”); for the second set, we also checked whether the additions or removals changed the set of entailments between versions. We discovered a high level of “merely syntactic” removals and additions. We develop a categorization of such changes based on a heuristic inference of the impact of the change. As a result, not only do we get a rich, purely analytic characterization of the change history of the NCIt, but also we generate a realistic test corpus for incremental classification.

The Unified Medical Language System (UMLS) combines many well-established authoritative medical informatics terminologies in one knowledge representation system. Such a resource is very valuable to the health care community and industry. However, the UMLS is very large and complex and poses serious comprehension problems for users and maintenance personnel. The authors present a representation to support the user's comprehension and navigation of the UMLS. An object-oriented database (OODB) representation is used to represent the two major components of the UMLS-the Metathesaurus and the Semantic Network-as a unified system. The semantic types of the Semantic Network are modeled as semantic type classes. Intersection classes are defined to model concepts of multiple semantic types, which are removed from the semantic type classes. The authors provide examples of how the intersection classes help expose omissions of concepts, highlight errors of semantic type classification, and uncover ambiguities of concepts in the UMLS. The resulting UMLS OODB schema is deeper and more refined than the Semantic Network, since intersection classes are introduced. The Metathesaurus is classified into more mutually exclusive, uniform sets of concepts. The schema improves the user's comprehension and navigation of the Metathesaurus. The UMLS OODB schema supports the user's comprehension and navigation of the Metathesaurus. It also helps expose and resolve modeling problems in the UMLS.

BackgroundWe describe development of the Early Childhood Caries (ECC) Basic Research Factors Questionnaire (BRFQ), a battery of measures assessing common potential predictors, mediators, and moderators of ECC. Individual-, family-, and community-level factors that are linked to oral health outcomes across at-risk populations are included. Developing standard measures of factors implicated in ECC has the potential to enhance our ability to understand mechanisms underlying successful prevention and to develop more effective interventions.MethodsThe Early Childhood Caries Collaborating Centers (EC4), funded by National Institute of Dental and Craniofacial Research, developed the BRFQ, which was used across four randomized trials to develop and test interventions for reducing ECC in at-risk populations. Forty-five investigators from across the centers and NIDCR were involved in the development process. Eight “measures working groups” identified relevant constructs and effective measurement approaches, which were then categorized as “essential” or “optional” common data elements (CDEs) for the EC4 projects.ResultsEssential CDEs include 88 items, with an additional 177 measures categorized as optional CDEs. Essential CDEs fell under the following domains: oral health knowledge, oral health behavior, utilization/insurance and cost, parent/caregiver dental self-efficacy, quality of life, caregiver and family characteristics, and child characteristics.ConclusionsThe BRFQ makes available a battery of measures that support efforts to understand population risk factors for ECC and to compare oral health outcomes across populations at risk. The BRFQ development process may be useful to other clinical research networks and consortia developing CDEs in other health research fields.Trial registrationAll the trial that used the BRFQ were registered at Clinicaltrial.gov NCT01116726, April 29, 2010; NCT01116739, May 3, 2010; NCT01129440, May 21, 2010; and NCT01205971, September 19, 2010.

The aim of this study is to recommend a common data element (CDE) to measure supervisory effectiveness of staff working in LTC homes that can be used in international research. Supervisory effectiveness can serve as a CDE in an effort to establish an international, person-centered LTC research infrastructure in accordance with the aims of the WE-THRIVE group (Worldwide Elements to Harmonize Research in Long Term Care Living Environments). A literature review was completed and then a panel of experts independently reviewed and prioritized appropriateness of the measures with mindfulness of their potential applications to international LTC settings. The selection of a recommended CDE measure was guided by the WE-THRIVE group’s focus on capacity rather than deficits, the expected availability of internationally comparable data and the goal to provide a short, ecologically viable measurement, specifically for low- and middle-income countries. Two measures were considered as the CDE for supervisory effectiveness, Benjamin Rose Relationship Scale and the Supervisory Support Scale; however, given that the latter measure has been translated in Spanish and Chinese and has been tested with nursing assistants in both of these countries with good psychometric properties, our group recommends it as the CDE going forward.

The Common Data Elements (CDEs) standard of the International organization for Standardization (ISO) 11179 is commonly used in the field of clinical data processing. The Biomedical Research Integrated Domain Group (BRIDG) model is the framework for biomedical and clinical research. Mapping CDEs to BRIDG (also known as CDE classification) would help with interoperability and data analysis in the field of clinical research. That said, manually mapping CDEs to their corresponding BRIDG class is highly time-consuming and labor-intensive. In this paper we present a new classification algorithm along with a new oversampling method. Our algorithm uses the Term Frequency-Inverse Document Frequency (TF-IDF) as the feature representation method. By assigning different weights to various attributes, we enable more important attributes to perform more important roles during the mapping process. In addition, the oversampling method generates every new attribute in the minor class by picking the length and setting the word of the new attribute according to the existing training set. Our research outcomes demonstrate significant contributions to the field in the following ways: (1) Generation of a new CDE classification algorithm that outperforms existing algorithms in the literature, including the Random Forest Classifier, Linear Support Vector Classification (SVC), Multinomial Naive Bayes (NB), Logistic Regression, and Long Short-Term Memory (LSTM) networks, in terms of accuracy, precision, recall, and F-1 score measures. (2) Generation of a new oversampling method able to improve CDE classification accuracy for Random Forest and Multinomial NB. (3) Our classification algorithm employs two novel attributes, namely “Data Element Preferred Definition” and “Document,” which are more efficient at classifying CDEs than the six attributes traditionally selected by domain experts.