METADATA MANAGEMENT FOR CLINICAL DATA INTEGRATION

Clinical data have been continuously collected and growing with the wide adoption of electronic health records (EHR). Clinical data have provided the foundation to facilitate state-of-art researches such as artificial intelligence in medicine. At the same time, it has become a challenge to integrate, access, and explore study-level patient data from large volumes of data from heterogeneous databases. Effective, fine-grained, cross-cohort data exploration, and semantically enabled approaches and systems are needed. To build semantically enabled systems, we need to leverage existing terminology systems and ontologies. Numerous ontologies have been developed recently and they play an important role in semantically enabled applications. Because they contain valuable codified knowledge, the management of these ontologies, as metadata, also requires systematic approaches. Moreover, in most clinical settings, patient data are collected with the help of a data dictionary. Knowledge of the relationships between an ontology and a related data dictionary is important for semantic interoperability. Such relationships are represented and maintained by mappings. Mappings store how data source elements and domain ontology concepts are linked, as well as how domain ontology concepts are linked between different ontologies. While mappings are crucial to the maintenance of relationships between an ontology and a related data dictionary, they are commonly captured by CSV files with limits capabilities for sharing, tracking, and visualization. The management of mappings requires an innovative, interactive, and collaborative approach. Metadata management servers to organize data that describes other data. In computer science and information science, ontology is the metadata consisting of the representation, naming, and definition of the hierarchies, properties, and relations between concepts. A structural, scalable, and computer understandable way for metadata management is critical to developing systems with the fine-grained data exploration capabilities. This dissertation presents a systematic approach called MetaSphere using metadata and ontologies to support the management and integration of clinical research data through our ontology-based metadata management system for multiple domains. MetaSphere is a general framework that aims to manage specific domain metadata, provide fine-grained data exploration interface, and store patient data in data warehouses. Moreover, MetaSphere provides a dedicated mapping interface called Interactive Mapping Interface (IMI) to map the data dictionary to well-recognized and standardized ontologies. MetaSphere has been applied to three domains successfully, sleep domain (X-search), pressure ulcer injuries and deep tissue pressure (SCIPUDSphere), and cancer. Specifically, MetaSphere stores domain ontology structurally in databases. Patient data in the corresponding domains are also stored in databases as data warehouses. MetaSphere provides a powerful query interface to enable interaction between human and actual patient data. Query interface is a mechanism allowing researchers to compose complex queries to pinpoint specific cohort over a large amount of patient data. The MetaSphere framework has been instantiated into three domains successfully and the detailed results are as below. X-search is publicly available at https://www.x-search.net with nine sleep domain datasets consisting of over 26,000 unique subjects. The canonical data dictionary contains over 900 common data elements across the datasets. X-search has received over 1800 cross-cohort queries by users from 16 countries. SCIPUDSphere has integrated a total number of 268,562 records containing 282 ICD9 codes related to pressure ulcer injuries among 36,626 individuals with spinal cord injuries. IMI is publicly available at http://epi-tome.com/. Using IMI, we have successfully mapped the North American Association of Central Cancer Registries (NAACCR) data dictionary to the National Cancer Institute Thesaurus (NCIt) concepts

Enriching information extraction pipelines in clinical decision support systems

Programa Oficial de Doutoramento en Tecnoloxías da Información e as Comunicacións. 5032V01[Resumo] Os estudos sanitarios de múltiples centros son importantes para aumentar a repercusión dos resultados da investigación médica debido ao número de suxeitos que poden participar neles. Para simplificar a execución destes estudos, o proceso de intercambio de datos debería ser sinxelo, por exemplo, mediante o uso de bases de datos interoperables. Con todo, a consecución desta interoperabilidade segue sendo un tema de investigación en curso, sobre todo debido aos problemas de gobernanza e privacidade dos datos. Na primeira fase deste traballo, propoñemos varias metodoloxías para optimizar os procesos de estandarización das bases de datos sanitarias. Este traballo centrouse na estandarización de fontes de datos heteroxéneas nun esquema de datos estándar, concretamente o OMOP CDM, que foi desenvolvido e promovido pola comunidade OHDSI. Validamos a nosa proposta utilizando conxuntos de datos de pacientes con enfermidade de Alzheimer procedentes de distintas institucións. Na seguinte etapa, co obxectivo de enriquecer a información almacenada nas bases de datos de OMOP CDM, investigamos solucións para extraer conceptos clínicos de narrativas non estruturadas, utilizando técnicas de recuperación de información e de procesamento da linguaxe natural. A validación realizouse a través de conxuntos de datos proporcionados en desafíos científicos, concretamente no National NLP Clinical Challenges(n2c2). Na etapa final, propuxémonos simplificar a execución de protocolos de estudos provenientes de múltiples centros, propoñendo solucións novas para perfilar, publicar e facilitar o descubrimento de bases de datos. Algunhas das solucións desenvolvidas están a utilizarse actualmente en tres proxectos europeos destinados a crear redes federadas de bases de datos de saúde en toda Europa.[Resumen] Los estudios sanitarios de múltiples centros son importantes para aumentar la repercusión de los resultados de la investigación médica debido al número de sujetos que pueden participar en ellos. Para simplificar la ejecución de estos estudios, el proceso de intercambio de datos debería ser sencillo, por ejemplo, mediante el uso de bases de datos interoperables. Sin embargo, la consecución de esta interoperabilidad sigue siendo un tema de investigación en curso, sobre todo debido a los problemas de gobernanza y privacidad de los datos. En la primera fase de este trabajo, proponemos varias metodologías para optimizar los procesos de estandarización de las bases de datos sanitarias. Este trabajo se centró en la estandarización de fuentes de datos heterogéneas en un esquema de datos estándar, concretamente el OMOP CDM, que ha sido desarrollado y promovido por la comunidad OHDSI. Validamos nuestra propuesta utilizando conjuntos de datos de pacientes con enfermedad de Alzheimer procedentes de distintas instituciones. En la siguiente etapa, con el objetivo de enriquecer la información almacenada en las bases de datos de OMOP CDM, hemos investigado soluciones para extraer conceptos clínicos de narrativas no estructuradas, utilizando técnicas de recuperación de información y de procesamiento del lenguaje natural. La validación se realizó a través de conjuntos de datos proporcionados en desafíos científicos, concretamente en el National NLP Clinical Challenges (n2c2). En la etapa final, nos propusimos simplificar la ejecución de protocolos de estudios provenientes de múltiples centros, proponiendo soluciones novedosas para perfilar, publicar y facilitar el descubrimiento de bases de datos. Algunas de las soluciones desarrolladas se están utilizando actualmente en tres proyectos europeos destinados a crear redes federadas de bases de datos de salud en toda Europa.[Abstract] Multicentre health studies are important to increase the impact of medical research findings due to the number of subjects that they are able to engage. To simplify the execution of these studies, the data-sharing process should be effortless, for instance, through the use of interoperable databases. However, achieving this interoperability is still an ongoing research topic, namely due to data governance and privacy issues. In the first stage of this work, we propose several methodologies to optimise the harmonisation pipelines of health databases. This work was focused on harmonising heterogeneous data sources into a standard data schema, namely the OMOP CDM which has been developed and promoted by the OHDSI community. We validated our proposal using data sets of Alzheimer’s disease patients from distinct institutions. In the following stage, aiming to enrich the information stored in OMOP CDM databases, we have investigated solutions to extract clinical concepts from unstructured narratives, using information retrieval and natural language processing techniques. The validation was performed through datasets provided in scientific challenges, namely in the National NLP Clinical Challenges (n2c2). In the final stage, we aimed to simplify the protocol execution of multicentre studies, by proposing novel solutions for profiling, publishing and facilitating the discovery of databases. Some of the developed solutions are currently being used in three European projects aiming to create federated networks of health databases across Europe

Web-Based Interactive Mapping from Data Dictionaries to Ontologies, with an Application to Cancer Registry

BACKGROUND: The Kentucky Cancer Registry (KCR) is a central cancer registry for the state of Kentucky that receives data about incident cancer cases from all healthcare facilities in the state within 6 months of diagnosis. Similar to all other U.S. and Canadian cancer registries, KCR uses a data dictionary provided by the North American Association of Central Cancer Registries (NAACCR) for standardized data entry. The NAACCR data dictionary is not an ontological system. Mapping between the NAACCR data dictionary and the National Cancer Institute (NCI) Thesaurus (NCIt) will facilitate the enrichment, dissemination and utilization of cancer registry data. We introduce a web-based system, called Interactive Mapping Interface (IMI), for creating mappings from data dictionaries to ontologies, in particular from NAACCR to NCIt. METHOD: IMI has been designed as a general approach with three components: (1) ontology library; (2) mapping interface; and (3) recommendation engine. The ontology library provides a list of ontologies as targets for building mappings. The mapping interface consists of six modules: project management, mapping dashboard, access control, logs and comments, hierarchical visualization, and result review and export. The built-in recommendation engine automatically identifies a list of candidate concepts to facilitate the mapping process. RESULTS: We report the architecture design and interface features of IMI. To validate our approach, we implemented an IMI prototype and pilot-tested features using the IMI interface to map a sample set of NAACCR data elements to NCIt concepts. 47 out of 301 NAACCR data elements have been mapped to NCIt concepts. Five branches of hierarchical tree have been identified from these mapped concepts for visual inspection. CONCLUSIONS: IMI provides an interactive, web-based interface for building mappings from data dictionaries to ontologies. Although our pilot-testing scope is limited, our results demonstrate feasibility using IMI for semantic enrichment of cancer registry data by mapping NAACCR data elements to NCIt concepts

A Knowledge Graph Framework for Dementia Research Data

Dementia disease research encompasses diverse data modalities, including advanced imaging, deep phenotyping, and multi-omics analysis. However, integrating these disparate data sources has historically posed a significant challenge, obstructing the unification and comprehensive analysis of collected information. In recent years, knowledge graphs have emerged as a powerful tool to address such integration issues by enabling the consolidation of heterogeneous data sources into a structured, interconnected network of knowledge. In this context, we introduce DemKG, an open-source framework designed to facilitate the construction of a knowledge graph integrating dementia research data, comprising three core components: a KG-builder that integrates diverse domain ontologies and data annotations, an extensions ontology providing necessary terms tailored for dementia research, and a versatile transformation module for incorporating study data. In contrast with other current solutions, our framework provides a stable foundation by leveraging established ontologies and community standards and simplifies study data integration while delivering solid ontology design patterns, broadening its usability. Furthermore, the modular approach of its components enhances flexibility and scalability. We showcase how DemKG might aid and improve multi-modal data investigations through a series of proof-of-concept scenarios focused on relevant Alzheimer’s disease biomarkers

Systematizing FAIR research data management in biomedical research projects: a data life cycle approach

Biomedical researchers are facing data management challenges brought by a new generation of data driven by the advent of translational medicine research. These challenges are further complicated by the recent calls for data re-use and long-term stewardship spearheaded by the FAIR principles initiative. As a result, there is an increasingly wide-spread recognition that advancing biomedical science is becoming dependent on the application of data science to manage and utilize highly diverse and complex data in ways that give it context, meaning, and longevity beyond its initial purpose. However, current methods and practices in biomedical informatics remain to adopt a traditional linear view of the informatics process (collect, store and analyse); focusing primarily on the challenges in data integration and analysis, which are challenges only pertaining to a part of the overall life cycle of research data. The aim of this research is to facilitate the adoption and integration of data management practices into the research life cycle of biomedical projects, thus improving their capabilities into solving data management-related challenges that they face throughout the course of their research work. To achieve this aim, this thesis takes a data life cycle approach to define and develop a systematic methodology and framework towards the systematization of FAIR data management in biomedical research projects. The overarching contribution of this research is the provision of a data-state life cycle model for research data management in Biomedical Translational Research Projects. This model provides insight into the dynamics between 1) the purpose of a research-driven data use case, 2) the data requirements that renders data in a state fit for purpose, 3) the data management functions that prepare and act upon data and 4) the resulting state of data that is _t to serve the use case. This insight led to the development of a FAIR data management framework, which is another contribution of this thesis. This framework provides data managers the groundwork, including the data models, resources and capabilities, needed to build a FAIR data management environment to manage data during the operational stages of a biomedical research project. An exemplary implementation of this architecture (PlatformTM) was developed and validated by real-world research datasets produced by collaborative research programs funded by the Innovative Medicine Initiative (IMI) BioVacSafe 1 , eTRIKS 2 and FAIRplus 3.Open Acces

Serviços de integração de dados para aplicações biomédicas

Doutoramento em Informática (MAP-i)In the last decades, the field of biomedical science has fostered unprecedented scientific advances. Research is stimulated by the constant evolution of information technology, delivering novel and diverse bioinformatics tools. Nevertheless, the proliferation of new and disconnected solutions has resulted in massive amounts of resources spread over heterogeneous and distributed platforms. Distinct data types and formats are generated and stored in miscellaneous repositories posing data interoperability challenges and delays in discoveries. Data sharing and integrated access to these resources are key features for successful knowledge extraction. In this context, this thesis makes contributions towards accelerating the semantic integration, linkage and reuse of biomedical resources. The first contribution addresses the connection of distributed and heterogeneous registries. The proposed methodology creates a holistic view over the different registries, supporting semantic data representation, integrated access and querying. The second contribution addresses the integration of heterogeneous information across scientific research, aiming to enable adequate data-sharing services. The third contribution presents a modular architecture to support the extraction and integration of textual information, enabling the full exploitation of curated data. The last contribution lies in providing a platform to accelerate the deployment of enhanced semantic information systems. All the proposed solutions were deployed and validated in the scope of rare diseases.Nas últimas décadas, o campo das ciências biomédicas proporcionou grandes avanços científicos estimulados pela constante evolução das tecnologias de informação. A criação de diversas ferramentas na área da bioinformática e a falta de integração entre novas soluções resultou em enormes quantidades de dados distribuídos por diferentes plataformas. Dados de diferentes tipos e formatos são gerados e armazenados em vários repositórios, o que origina problemas de interoperabilidade e atrasa a investigação. A partilha de informação e o acesso integrado a esses recursos são características fundamentais para a extração bem sucedida do conhecimento científico. Nesta medida, esta tese fornece contribuições para acelerar a integração, ligação e reutilização semântica de dados biomédicos. A primeira contribuição aborda a interconexão de registos distribuídos e heterogéneos. A metodologia proposta cria uma visão holística sobre os diferentes registos, suportando a representação semântica de dados e o acesso integrado. A segunda contribuição aborda a integração de diversos dados para investigações científicas, com o objetivo de suportar serviços interoperáveis para a partilha de informação. O terceiro contributo apresenta uma arquitetura modular que apoia a extração e integração de informações textuais, permitindo a exploração destes dados. A última contribuição consiste numa plataforma web para acelerar a criação de sistemas de informação semânticos. Todas as soluções propostas foram validadas no âmbito das doenças raras

Arquiteturas federadas para integração de dados biomédicos

Doutoramento Ciências da ComputaçãoThe last decades have been characterized by a continuous adoption of IT solutions in the healthcare sector, which resulted in the proliferation of tremendous amounts of data over heterogeneous systems. Distinct data types are currently generated, manipulated, and stored, in the several institutions where patients are treated. The data sharing and an integrated access to this information will allow extracting relevant knowledge that can lead to better diagnostics and treatments. This thesis proposes new integration models for gathering information and extracting knowledge from multiple and heterogeneous biomedical sources. The scenario complexity led us to split the integration problem according to the data type and to the usage specificity. The first contribution is a cloud-based architecture for exchanging medical imaging services. It offers a simplified registration mechanism for providers and services, promotes remote data access, and facilitates the integration of distributed data sources. Moreover, it is compliant with international standards, ensuring the platform interoperability with current medical imaging devices. The second proposal is a sensor-based architecture for integration of electronic health records. It follows a federated integration model and aims to provide a scalable solution to search and retrieve data from multiple information systems. The last contribution is an open architecture for gathering patient-level data from disperse and heterogeneous databases. All the proposed solutions were deployed and validated in real world use cases.A adoção sucessiva das tecnologias de comunicação e de informação na área da saúde tem permitido um aumento na diversidade e na qualidade dos serviços prestados, mas, ao mesmo tempo, tem gerado uma enorme quantidade de dados, cujo valor científico está ainda por explorar. A partilha e o acesso integrado a esta informação poderá permitir a identificação de novas descobertas que possam conduzir a melhores diagnósticos e a melhores tratamentos clínicos. Esta tese propõe novos modelos de integração e de exploração de dados com vista à extração de conhecimento biomédico a partir de múltiplas fontes de dados. A primeira contribuição é uma arquitetura baseada em nuvem para partilha de serviços de imagem médica. Esta solução oferece um mecanismo de registo simplificado para fornecedores e serviços, permitindo o acesso remoto e facilitando a integração de diferentes fontes de dados. A segunda proposta é uma arquitetura baseada em sensores para integração de registos electrónicos de pacientes. Esta estratégia segue um modelo de integração federado e tem como objetivo fornecer uma solução escalável que permita a pesquisa em múltiplos sistemas de informação. Finalmente, o terceiro contributo é um sistema aberto para disponibilizar dados de pacientes num contexto europeu. Todas as soluções foram implementadas e validadas em cenários reais

Composição de serviços para aplicações biomédicas

Doutoramento em Engenharia InformáticaA exigente inovação na área das aplicações biomédicas tem guiado a evolução das tecnologias de informação nas últimas décadas. Os desafios associados a uma gestão, integração, análise e interpretação eficientes dos dados provenientes das mais modernas tecnologias de hardware e software requerem um esforço concertado. Desde hardware para sequenciação de genes a registos electrónicos de paciente, passando por pesquisa de fármacos, a possibilidade de explorar com precisão os dados destes ambientes é vital para a compreensão da saúde humana. Esta tese engloba a discussão e o desenvolvimento de melhores estratégias informáticas para ultrapassar estes desafios, principalmente no contexto da composição de serviços, incluindo técnicas flexíveis de integração de dados, como warehousing ou federação, e técnicas avançadas de interoperabilidade, como serviços web ou LinkedData. A composição de serviços é apresentada como um ideal genérico, direcionado para a integração de dados e para a interoperabilidade de software. Relativamente a esta última, esta investigação debruçou-se sobre o campo da farmacovigilância, no contexto do projeto Europeu EU-ADR. As contribuições para este projeto, um novo standard de interoperabilidade e um motor de execução de workflows, sustentam a sucesso da EU-ADR Web Platform, uma plataforma para realizar estudos avançados de farmacovigilância. No contexto do projeto Europeu GEN2PHEN, esta investigação visou ultrapassar os desafios associados à integração de dados distribuídos e heterogéneos no campo do varíoma humano. Foi criada uma nova solução, WAVe - Web Analyses of the Variome, que fornece uma coleção rica de dados de variação genética através de uma interface Web inovadora e de uma API avançada. O desenvolvimento destas estratégias evidenciou duas oportunidades claras na área de software biomédico: melhorar o processo de implementação de software através do recurso a técnicas de desenvolvimento rápidas e aperfeiçoar a qualidade e disponibilidade dos dados através da adopção do paradigma de web semântica. A plataforma COEUS atravessa as fronteiras de integração e interoperabilidade, fornecendo metodologias para a aquisição e tradução flexíveis de dados, bem como uma camada de serviços interoperáveis para explorar semanticamente os dados agregados. Combinando as técnicas de desenvolvimento rápidas com a riqueza da perspectiva "Semantic Web in a box", a plataforma COEUS é uma aproximação pioneira, permitindo o desenvolvimento da próxima geração de aplicações biomédicas.The demand for innovation in the biomedical software domain has been an information technologies evolution driver over the last decades. The challenges associated with the effective management, integration, analyses and interpretation of the wealth of life sciences information stemming from modern hardware and software technologies require concerted efforts. From gene sequencing hardware to pharmacology research up to patient electronic health records, the ability to accurately explore data from these environments is vital to further improve our understanding of human health. This thesis encloses the discussion on building better informatics strategies to address these challenges, primarily in the context of service composition, including warehousing and federation strategies for resource integration, as well as web services or LinkedData for software interoperability. Service composition is introduced as a general principle, geared towards data integration and software interoperability. Concerning the latter, this research covers the service composition requirements within the pharmacovigilance field, namely on the European EU-ADR project. The contributions to this area, the definition of a new interoperability standard and the creation of a new workflow-wrapping engine, are behind the successful construction of the EUADR Web Platform, a workspace for delivering advanced pharmacovigilance studies. In the context of the European GEN2PHEN project, this research tackles the challenges associated with the integration of heterogeneous and distributed data in the human variome field. For this matter, a new lightweight solution was created: WAVe, Web Analysis of the Variome, provides a rich collection of genetic variation data through an innovative portal and an advanced API. The development of the strategies underlying these products highlighted clear opportunities in the biomedical software field: enhancing the software implementation process with rapid application development approaches and improving the quality and availability of data with the adoption of the Semantic Web paradigm. COEUS crosses the boundaries of integration and interoperability as it provides a framework for the flexible acquisition and translation of data into a semantic knowledge base, as well as a comprehensive set of interoperability services, from REST to LinkedData, to fully exploit gathered data semantically. By combining the lightness of rapid application development strategies with the richness of its "Semantic Web in a box" approach, COEUS is a pioneering framework to enhance the development of the next generation of biomedical applications

Resource discovery in heterogeneous digital content environments

The concept of 'resource discovery' is central to our understanding of how users explore, navigate, locate and retrieve information resources. This submission for a PhD by Published Works examines a series of 11 related works which explore topics pertaining to resource discovery, each demonstrating heterogeneity in their digital discovery context. The assembled works are prefaced by nine chapters which seek to review and critically analyse the contribution of each work, as well as provide contextualization within the wider body of research literature. A series of conceptual sub-themes is used to organize and structure the works and the accompanying critical commentary. The thesis first begins by examining issues in distributed discovery contexts by studying collection level metadata (CLM), its application in 'information landscaping' techniques, and its relationship to the efficacy of federated item-level search tools. This research narrative continues but expands in the later works and commentary to consider the application of Knowledge Organization Systems (KOS), particularly within Semantic Web and machine interface contexts, with investigations of semantically aware terminology services in distributed discovery. The necessary modelling of data structures to support resource discovery - and its associated functionalities within digital libraries and repositories - is then considered within the novel context of technology-supported curriculum design repositories, where questions of human-computer interaction (HCI) are also examined. The final works studied as part of the thesis are those which investigate and evaluate the efficacy of open repositories in exposing knowledge commons to resource discovery via web search agents. Through the analysis of the collected works it is possible to identify a unifying theory of resource discovery, with the proposed concept of (meta)data alignment described and presented with a visual model. This analysis assists in the identification of a number of research topics worthy of further research; but it also highlights an incremental transition by the present author, from using research to inform the development of technologies designed to support or facilitate resource discovery, particularly at a 'meta' level, to the application of specific technologies to address resource discovery issues in a local context. Despite this variation the research narrative has remained focussed on topics surrounding resource discovery in heterogeneous digital content environments and is noted as having generated a coherent body of work. Separate chapters are used to consider the methodological approaches adopted in each work and the contribution made to research knowledge and professional practice.The concept of 'resource discovery' is central to our understanding of how users explore, navigate, locate and retrieve information resources. This submission for a PhD by Published Works examines a series of 11 related works which explore topics pertaining to resource discovery, each demonstrating heterogeneity in their digital discovery context. The assembled works are prefaced by nine chapters which seek to review and critically analyse the contribution of each work, as well as provide contextualization within the wider body of research literature. A series of conceptual sub-themes is used to organize and structure the works and the accompanying critical commentary. The thesis first begins by examining issues in distributed discovery contexts by studying collection level metadata (CLM), its application in 'information landscaping' techniques, and its relationship to the efficacy of federated item-level search tools. This research narrative continues but expands in the later works and commentary to consider the application of Knowledge Organization Systems (KOS), particularly within Semantic Web and machine interface contexts, with investigations of semantically aware terminology services in distributed discovery. The necessary modelling of data structures to support resource discovery - and its associated functionalities within digital libraries and repositories - is then considered within the novel context of technology-supported curriculum design repositories, where questions of human-computer interaction (HCI) are also examined. The final works studied as part of the thesis are those which investigate and evaluate the efficacy of open repositories in exposing knowledge commons to resource discovery via web search agents. Through the analysis of the collected works it is possible to identify a unifying theory of resource discovery, with the proposed concept of (meta)data alignment described and presented with a visual model. This analysis assists in the identification of a number of research topics worthy of further research; but it also highlights an incremental transition by the present author, from using research to inform the development of technologies designed to support or facilitate resource discovery, particularly at a 'meta' level, to the application of specific technologies to address resource discovery issues in a local context. Despite this variation the research narrative has remained focussed on topics surrounding resource discovery in heterogeneous digital content environments and is noted as having generated a coherent body of work. Separate chapters are used to consider the methodological approaches adopted in each work and the contribution made to research knowledge and professional practice

Doctor of Philosophy

dissertationBiomedical data are a rich source of information and knowledge. Not only are they useful for direct patient care, but they may also offer answers to important population-based questions. Creating an environment where advanced analytics can be performed against biomedical data is nontrivial, however. Biomedical data are currently scattered across multiple systems with heterogeneous data, and integrating these data is a bigger task than humans can realistically do by hand; therefore, automatic biomedical data integration is highly desirable but has never been fully achieved. This dissertation introduces new algorithms that were devised to support automatic and semiautomatic integration of heterogeneous biomedical data. The new algorithms incorporate both data mining and biomedical informatics techniques to create "concept bags" that are used to compute similarity between data elements in the same way that "word bags" are compared in data mining. Concept bags are composed of controlled medical vocabulary concept codes that are extracted from text using named-entity recognition software. To test the new algorithm, three biomedical text similarity use cases were examined: automatically aligning data elements between heterogeneous data sets, determining degrees of similarity between medical terms using a published benchmark, and determining similarity between ICU discharge summaries. The method is highly configurable and 5 different versions were tested. The concept bag method performed particularly well aligning data elements and outperformed the compared algorithms by iv more than 5%. Another configuration that included hierarchical semantics performed particularly well at matching medical terms, meeting or exceeding 30 of 31 other published results using the same benchmark. Results for the third scenario of computing ICU discharge summary similarity were less successful. Correlations between multiple methods were low, including between terminologists. The concept bag algorithms performed consistently and comparatively well and appear to be viable options for multiple scenarios. New applications of the method and ideas for improving the algorithm are being discussed for future work, including several performance enhancements, configuration-based enhancements, and concept vector weighting using the TF-IDF formulas