Radiation exposure and breast cancer: lessons from Chernobyl.

The lessons learned from the Chernobyl disaster have become increasingly important after the second anniversary of the Fukushima, Japan nuclear accident. Historically, data from the Chernobyl reactor accident 27 years ago demonstrated a strong correlation with thyroid cancer, but data on the radiation effects of Chernobyl on breast cancer incidence have remained inconclusive. We reviewed the published literature on the effects of the Chernobyl disaster on breast cancer incidence, using Medline and Scopus from the time of the accident to December of 2010. Our findings indicate limited data and statistical flaws. Other confounding factors, such as discrepancies in data collection, make interpretation of the results from the published literature difficult. Re-analyzing the data reveals that the incidence of breast cancer in Chernobyl-disaster-exposed women could be higher than previously thought. We have learned little of the consequences of radiation exposure at Chernobyl except for its effects on thyroid cancer incidence. Marking the 27th year after the Chernobyl event, this report sheds light on a specific, crucial and understudied aspect of the results of radiation from a gruesome nuclear power plant disaster.

Requirements for guidelines systems: implementation challenges and lessons from existing software-engineering efforts.

BACKGROUND: A large body of work in the clinical guidelines field has identified requirements for guideline systems, but there are formidable challenges in translating such requirements into production-quality systems that can be used in routine patient care. Detailed analysis of requirements from an implementation perspective can be useful in helping define sub-requirements to the point where they are implementable. Further, additional requirements emerge as a result of such analysis. During such an analysis, study of examples of existing, software-engineering efforts in non-biomedical fields can provide useful signposts to the implementer of a clinical guideline system. METHODS: In addition to requirements described by guideline-system authors, comparative reviews of such systems, and publications discussing information needs for guideline systems and clinical decision support systems in general, we have incorporated additional requirements related to production-system robustness and functionality from publications in the business workflow domain, in addition to drawing on our own experience in the development of the Proteus guideline system (http://proteme.org). RESULTS: The sub-requirements are discussed by conveniently grouping them into the categories used by the review of Isern and Moreno 2008. We cite previous work under each category and then provide sub-requirements under each category, and provide example of similar work in software-engineering efforts that have addressed a similar problem in a non-biomedical context. CONCLUSIONS: When analyzing requirements from the implementation viewpoint, knowledge of successes and failures in related software-engineering efforts can guide implementers in the choice of effective design and development strategies.

Determining correspondences between high-frequency MedDRA concepts and SNOMED: a case study.

BACKGROUND: The Systematic Nomenclature of Medicine Clinical Terms (SNOMED CT) is being advocated as the foundation for encoding clinical documentation. While the electronic medical record is likely to play a critical role in pharmacovigilance - the detection of adverse events due to medications - classification and reporting of Adverse Events is currently based on the Medical Dictionary of Regulatory Activities (MedDRA). Complete and high-quality MedDRA-to-SNOMED CT mappings can therefore facilitate pharmacovigilance. The existing mappings, as determined through the Unified Medical Language System (UMLS), are partial, and record only one-to-one correspondences even though SNOMED CT can be used compositionally. Efforts to map previously unmapped MedDRA concepts would be most productive if focused on concepts that occur frequently in actual adverse event data. We aimed to identify aspects of MedDRA that complicate mapping to SNOMED CT, determine pattern in unmapped high-frequency MedDRA concepts, and to identify types of integration errors in the mapping of MedDRA to UMLS. METHODS: Using one years' data from the US Federal Drug Administrations Adverse Event Reporting System, we identified MedDRA preferred terms that collectively accounted for 95% of both Adverse Events and Therapeutic Indications records. After eliminating those already mapping to SNOMED CT, we attempted to map the remaining 645 Adverse-Event and 141 Therapeutic-Indications preferred terms with software assistance. RESULTS: All but 46 Adverse-Event and 7 Therapeutic-Indications preferred terms could be composed using SNOMED CT concepts: none of these required more than 3 SNOMED CT concepts to compose. We describe the common composition patterns in the paper. About 30% of both Adverse-Event and Therapeutic-Indications Preferred Terms corresponded to single SNOMED CT concepts: the correspondence was detectable by human inspection but had been missed during the integration process, which had created duplicated concepts in UMLS. CONCLUSIONS: Identification of composite mapping patterns, and the types of errors that occur in the MedDRA content within UMLS, can focus larger-scale efforts on improving the quality of such mappings, which may assist in the creation of an adverse-events ontology.

Informatics tools to improve clinical research study implementation.

BACKGROUND: There are numerous potential sources of problems when performing complex clinical research trials. These issues are compounded when studies are multi-site and multiple personnel from different sites are responsible for varying actions from case report form design to primary data collection and data entry. METHODS: We describe an approach that emphasizes the use of a variety of informatics tools that can facilitate study coordination, training, data checks and early identification and correction of faulty procedures and data problems. The paper focuses on informatics tools that can help in case report form design, procedures and training and data management. CONCLUSION: Informatics tools can be used to facilitate study coordination and implementation of clinical research trials.

Metadata-driven Delphi rating on the Internet.

Paper-based data collection and analysis for consensus development is inefficient and error-prone. Computerized techniques that could improve efficiency, however, have been criticized as costly, inconvenient and difficult to use. We designed and implemented a metadata-driven Web-based Delphi rating and analysis tool, employing the flexible entity-attribute-value schema to create generic, reusable software. The software can be applied to various domains by altering the metadata; the programming code remains intact. This approach greatly reduces the marginal cost of re-using the software. We implemented our software to prepare for the Conference on Guidelines Standardization. Twenty-three invited experts completed the first round of the Delphi rating on the Web. For each participant, the software generated individualized reports that described the median rating and the disagreement index (calculated from the Interpercentile Range Adjusted for Symmetry) as defined by the RAND/UCLA Appropriateness Method. We evaluated the software with a satisfaction survey using a five-level Likert scale. The panelists felt that Web data entry was convenient (median 4, interquartile range [IQR] 4.0-5.0), acceptable (median 4.5, IQR 4.0-5.0) and easily accessible (median 5, IQR 4.0-5.0). We conclude that Web-based Delphi rating for consensus development is a convenient and acceptable alternative to the traditional paper-based method.

Text mining neuroscience journal articles to populate neuroscience databases.

We have developed a program NeuroText to populate the neuroscience databases in SenseLab (http://senselab.med.yale.edu/senselab) by mining the natural language text of neuroscience articles. NeuroText uses a two-step approach to identify relevant articles. The first step (pre-processing), aimed at 100% sensitivity, identifies abstracts containing database keywords. In the second step, potentially relevant abstracts identified in the first step are processed for specificity dictated by database architecture, and neuroscience, lexical and semantic contexts. NeuroText results were presented to the experts for validation using a dynamically generated interface that also allows expert-validated articles to be automatically deposited into the databases. Of the test set of 912 articles, 735 were rejected at the pre-processing step. For the remaining articles, the accuracy of predicting database-relevant articles was 85%. Twenty-two articles were erroneously identified. NeuroText deferred decisions on 29 articles to the expert. A comparison of NeuroText results versus the experts' analyses revealed that the program failed to correctly identify articles' relevance due to concepts that did not yet exist in the knowledgebase or due to vaguely presented information in the abstracts. NeuroText uses two "evolution" techniques (supervised and unsupervised) that play an important role in the continual improvement of the retrieval results. Software that uses the NeuroText approach can facilitate the creation of curated, special-interest, bibliography databases.

Metadata-driven ad hoc query of patient data: meeting the needs of clinical studies.

Clinical study data management systems (CSDMSs) have many similarities to clinical patient record systems (CPRSs) in their focus on recording clinical parameters. Requirements for ad hoc query interfaces for both systems would therefore appear to be highly similar. However, a clinical study is concerned primarily with collective responses of groups of subjects to standardized therapeutic interventions for the same underlying clinical condition. The parameters that are recorded in CSDMSs tend to be more diverse than those required for patient management in non-research settings, because of the greater emphasis on questionnaires for which responses to each question are recorded separately. The differences between CSDMSs and CPRSs are reflected in the metadata that support the respective systems' operation, and need to be reflected in the query interfaces. The authors describe major revisions of their previously described CSDMS ad hoc query interface to meet CSDMS needs more fully, as well as its porting to a Web-based platform.

Managing complex change in clinical study metadata.

In highly functional metadata-driven software, the interrelationships within the metadata become complex, and maintenance becomes challenging. We describe an approach to metadata management that uses a knowledge-base subschema to store centralized information about metadata dependencies and use cases involving specific types of metadata modification. Our system borrows ideas from production-rule systems in that some of this information is a high-level specification that is interpreted and executed dynamically by a middleware engine. Our approach is implemented in TrialDB, a generic clinical study data management system. We review approaches that have been used for metadata management in other contexts and describe the features, capabilities, and limitations of our system.

Achieving evolvable Web-database bioscience applications using the EAV/CR framework: recent advances.

The EAV/CR framework, designed for database support of rapidly evolving scientific domains, utilizes metadata to facilitate schema maintenance and automatic generation of Web-enabled browsing interfaces to the data. EAV/CR is used in SenseLab, a neuroscience database that is part of the national Human Brain Project. This report describes various enhancements to the framework. These include (1) the ability to create "portals" that present different subsets of the schema to users with a particular research focus, (2) a generic XML-based protocol to assist data extraction and population of the database by external agents, (3) a limited form of ad hoc data query, and (4) semantic descriptors for interclass relationships and links to controlled vocabularies such as the UMLS.

Temporal query of attribute-value patient data: utilizing the constraints of clinical studies.

We describe an interface and architecture for ad hoc temporal query of TrialDB, a clinical study data management system (CSDMS). A clinical study focuses primarily on the effect of therapy on a group of patients, who have individually enrolled in a study at different times. Relative times (chronological offsets from the time of enrollment) are therefore more useful than absolute times when collectively describing therapeutic or adverse events. For logistic reasons, study parameter values are typically recorded at fixed relative times ('study events'), which serve as time-stamps and can be used by CSDMS temporal query algorithms to simplify temporal computations. The entity-attribute-value model of clinical data storage, used by both CSDMSs and clinical patient record systems, complicates temporal query. To apply temporal operators, data for parameters of interest must first be transiently converted into conventional relational form, with one column per parameter.

Metadata-driven creation of data marts from an EAV-modeled clinical research database.

Generic clinical study data management systems can record data on an arbitrary number of parameters in an arbitrary number of clinical studies without requiring modification of the database schema. They achieve this by using an Entity-Attribute-Value (EAV) model for clinical data. While very flexible for creating transaction-oriented systems for data entry and browsing of individual forms, EAV-modeled data is unsuitable for direct analytical processing, which is the focus of data marts. For this purpose, such data must be extracted and restructured appropriately. This paper describes how such a process, which is non-trivial and highly error prone if performed using non-systematic approaches, can be automated by judicious use of the study metadata-the descriptions of measured parameters and their higher-level grouping. The metadata, in addition to driving the process, is exported along with the data, in order to facilitate its human interpretation.

The Greater Plains Collaborative: a PCORnet Clinical Research Data Network.

The Greater Plains Collaborative (GPC) is composed of 10 leading medical centers repurposing the research programs and informatics infrastructures developed through Clinical and Translational Science Award initiatives. Partners are the University of Kansas Medical Center, Children's Mercy Hospital, University of Iowa Healthcare, the University of Wisconsin-Madison, the Medical College of Wisconsin and Marshfield Clinic, the University of Minnesota Academic Health Center, the University of Nebraska Medical Center, the University of Texas Health Sciences Center at San Antonio, and the University of Texas Southwestern Medical Center. The GPC network brings together a diverse population of 10 million people across 1300 miles covering seven states with a combined area of 679 159 square miles. Using input from community members, breast cancer was selected as a focus for cohort building activities. In addition to a high-prevalence disorder, we also selected a rare disease, amyotrophic lateral sclerosis.

An eUtils toolset and its use for creating a pipeline to link genomics and proteomics analyses to domain-specific biomedical literature.

BACKGROUND: Numerous biomedical software applications access databases maintained by the US National Center for Biotechnology Information (NCBI). To ease software automation, NCBI provides a powerful but complex Web-service-based programming interface, eUtils. This paper describes a toolset that simplifies eUtils use through a graphical front-end that can be used by non-programmers to construct data-extraction pipelines. The front-end relies on a code library that provides high-level wrappers around eUtils functions, and which is distributed as open-source, allowing customization and enhancement by individuals with programming skills. METHODS: We initially created an application that queried eUtils to retrieve nephrology-specific biomedical literature citations for a user-definable set of genes. We later augmented the application code to create a general-purpose library that accesses eUtils capability as individual functions that could be combined into user-defined pipelines. RESULTS: The toolset's use is illustrated with an application that serves as a front-end to the library and can be used by non-programmers to construct user-defined pipelines. The operation of the library is illustrated for the literature-surveillance application, which serves as a case-study. An overview of the library is also provided. CONCLUSIONS: The library simplifies use of the eUtils service by operating at a higher level, and also transparently addresses robustness issues that would need to be individually implemented otherwise, such as error recovery and prevention of overloading of the eUtils service.

Natural language processing: an introduction.

OBJECTIVES: To provide an overview and tutorial of natural language processing (NLP) and modern NLP-system design. TARGET AUDIENCE: This tutorial targets the medical informatics generalist who has limited acquaintance with the principles behind NLP and/or limited knowledge of the current state of the art. SCOPE: We describe the historical evolution of NLP, and summarize common NLP sub-problems in this extensive field. We then provide a synopsis of selected highlights of medical NLP efforts. After providing a brief description of common machine-learning approaches that are being used for diverse NLP sub-problems, we discuss how modern NLP architectures are designed, with a summary of the Apache Foundation's Unstructured Information Management Architecture. We finally consider possible future directions for NLP, and reflect on the possible impact of IBM Watson on the medical field.

Leveraging a clinical research information system to assist biospecimen data and workflow management: a hybrid approach.

BACKGROUND: Large multi-center clinical studies often involve the collection and analysis of biological samples. It is necessary to ensure timely, complete and accurate recording of analytical results and associated phenotypic and clinical information. The TRIBE-AKI Consortium http://www.yale.edu/tribeaki supports a network of multiple related studies and sample biorepository, thus allowing researchers to take advantage of a larger specimen collection than they might have at an individual institution. DESCRIPTION: We describe a biospecimen data management system (BDMS) that supports TRIBE-AKI and is intended for multi-center collaborative clinical studies that involve shipment of biospecimens between sites. This system works in conjunction with a clinical research information system (CRIS) that stores the clinical data associated with the biospecimens, along with other patient-related parameters. Inter-operation between the two systems is mediated by an interactively invoked suite of Web Services, as well as by batch code. We discuss various challenges involved in integration. CONCLUSIONS: Our experience indicates that an approach that emphasizes inter-operability is reasonably optimal in allowing each system to be utilized for the tasks for which it is best suited.

Drug safety surveillance using de-identified EMR and claims data: issues and challenges.

The author discusses the challenges of pharmacovigilance using electronic medical record and claims data. Use of ICD-9 encoded data has low sensitivity for detection of adverse drug events (ADEs), because it requires that an ADE escalate to major-complaint level before it can be identified, and because clinical symptomatology is relatively under-represented in ICD-9. A more appropriate vocabulary for ADE identification, SNOMED CT, awaits wider deployment. The narrative-text record of progress notes can potentially be used for more sensitive ADE detection. More effective surveillance will require the ability to grade ADEs by severity. Finally, access to online drug information that includes both a reliable hierarchy of drug families as well as structured information on existing ADEs can improve the focus and predictive ability of surveillance efforts.

Migrating existing clinical content from ICD-9 to SNOMED.

OBJECTIVE: To identify challenges in mapping internal International Classification of Disease, 9th edition, Clinical Modification (ICD-9-CM) encoded legacy data to Systematic Nomenclature of Medicine (SNOMED), using SNOMED-prescribed compositional approaches where appropriate, and to explore the mapping coverage provided by the US National Library of Medicine (NLM)'s SNOMED clinical core subset. DESIGN: This study selected ICD-CM codes that occurred at least 100 times in the organization's problem list or diagnosis data in 2008. After eliminating codes whose exact mappings were already available in UMLS, the remainder were mapped manually with software assistance. RESULTS: Of the 2194 codes, 784 (35.7%) required manual mapping. 435 of these represented concept types documented in SNOMED as deprecated: these included the qualifying phrases such as 'not elsewhere classified'. A third of the codes were composite, requiring multiple SNOMED code to map. Representing 45 composite concepts required introducing disjunction ('or') or set-difference ('without') operators, which are not currently defined in SNOMED. Only 47% of the concepts required for composition were present in the clinical core subset. Search of SNOMED for the correct concepts often required extensive application of knowledge of both English and medical synonymy. CONCLUSION: Strategies to deal with legacy ICD data must address the issue of codes created by non-taxonomist users. The NLM core subset possibly needs augmentation with concepts from certain SNOMED hierarchies, notably qualifiers, body structures, substances/products and organisms. Concept-matching software needs to utilize query expansion strategies, but these may be effective in production settings only if a large but non-redundant SNOMED subset that minimizes the proportion of extensively pre-coordinated concepts is also available.