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

Knowledge-based Biomedical Data Science 2019

Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 table

Garantia de privacidade na exploração de bases de dados distribuídas

Anonymisation is currently one of the biggest challenges when sharing sensitive personal information. Its importance depends largely on the application domain, but when dealing with health information, this becomes a more serious issue. A simpler approach to avoid this disclosure is to ensure that all data that can be associated directly with an individual is removed from the original dataset. However, some studies have shown that simple anonymisation procedures can sometimes be reverted using specific patients’ characteristics, namely when the anonymisation is based on hidden key attributes. In this work, we propose a secure architecture to share information from distributed databases without compromising the subjects’ privacy. The work was initially focused on identifying techniques to link information between multiple data sources, in order to revert the anonymization procedures. In a second phase, we developed the methodology to perform queries over distributed databases was proposed. The architecture was validated using a standard data schema that is widely adopted in observational research studies.A garantia da anonimização de dados é atualmente um dos maiores desafios quando existe a necessidade de partilhar informações pessoais de carácter sensível. Apesar de ser um problema transversal a muitos domínios de aplicação, este torna-se mais crítico quando a anonimização envolve dados clinicos. Nestes casos, a abordagem mais comum para evitar a divulgação de dados, que possam ser associados diretamente a um indivíduo, consiste na remoção de atributos identificadores. No entanto, segundo a literatura, esta abordagem não oferece uma garantia total de anonimato, que pode ser quebrada através de ataques específicos que permitem a reidentificação dos sujeitos. Neste trabalho, é proposta uma arquitetura que permite partilhar dados armazenados em repositórios distribuídos, de forma segura e sem comprometer a privacidade. Numa primeira fase deste trabalho, foi feita uma análise de técnicas que permitam reverter os procedimentos de anonimização. Na fase seguinte, foi proposta uma metodologia que permite realizar pesquisas em bases de dados distribuídas, sem que o anonimato seja quebrado. Esta arquitetura foi validada sobre um esquema de base de dados relacional que é amplamente utilizado em estudos clínicos observacionais.Mestrado em Ciberseguranç

Storage Solutions for Big Data Systems: A Qualitative Study and Comparison

Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main data models namely document oriented, key value, graph and wide column. Moreover, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice. Typically, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings forth second facet of big data storage, big data file formats, into picture. The second half of the research paper compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Decentralized storage and blockchain are seen as the next generation of big data storage and its challenges and future prospects have also been discussed

A Learning Health System for Radiation Oncology

The proposed research aims to address the challenges faced by clinical data science researchers in radiation oncology accessing, integrating, and analyzing heterogeneous data from various sources. The research presents a scalable intelligent infrastructure, called the Health Information Gateway and Exchange (HINGE), which captures and structures data from multiple sources into a knowledge base with semantically interlinked entities. This infrastructure enables researchers to mine novel associations and gather relevant knowledge for personalized clinical outcomes. The dissertation discusses the design framework and implementation of HINGE, which abstracts structured data from treatment planning systems, treatment management systems, and electronic health records. It utilizes disease-specific smart templates for capturing clinical information in a discrete manner. HINGE performs data extraction, aggregation, and quality and outcome assessment functions automatically, connecting seamlessly with local IT/medical infrastructure. Furthermore, the research presents a knowledge graph-based approach to map radiotherapy data to an ontology-based data repository using FAIR (Findable, Accessible, Interoperable, Reusable) concepts. This approach ensures that the data is easily discoverable and accessible for clinical decision support systems. The dissertation explores the ETL (Extract, Transform, Load) process, data model frameworks, ontologies, and provides a real-world clinical use case for this data mapping. To improve the efficiency of retrieving information from large clinical datasets, a search engine based on ontology-based keyword searching and synonym-based term matching tool was developed. The hierarchical nature of ontologies is leveraged to retrieve patient records based on parent and children classes. Additionally, patient similarity analysis is conducted using vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) to identify similar patients based on text corpus creation methods. Results from the analysis using these models are presented. The implementation of a learning health system for predicting radiation pneumonitis following stereotactic body radiotherapy is also discussed. 3D convolutional neural networks (CNNs) are utilized with radiographic and dosimetric datasets to predict the likelihood of radiation pneumonitis. DenseNet-121 and ResNet-50 models are employed for this study, along with integrated gradient techniques to identify salient regions within the input 3D image dataset. The predictive performance of the 3D CNN models is evaluated based on clinical outcomes. Overall, the proposed Learning Health System provides a comprehensive solution for capturing, integrating, and analyzing heterogeneous data in a knowledge base. It offers researchers the ability to extract valuable insights and associations from diverse sources, ultimately leading to improved clinical outcomes. This work can serve as a model for implementing LHS in other medical specialties, advancing personalized and data-driven medicine

Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

Using the ResearchEHR platform to facilitate the practical application of the EHR standards

Possibly the most important requirement to support co-operative work among health professionals and institutions is the ability of sharing EHRs in a meaningful way, and it is widely acknowledged that standardization of data and concepts is a prerequisite to achieve semantic interoperability in any domain. Different international organizations are working on the definition of EHR architectures but the lack of tools that implement them hinders their broad adoption. In this paper we present ResearchEHR, a software platform whose objective is to facilitate the practical application of EHR standards as a way of reaching the desired semantic interoperability. This platform is not only suitable for developing new systems but also for increasing the standardization of existing ones. The work reported here describes how the platform allows for the edition, validation, and search of archetypes, converts legacy data into normalized, archetypes extracts, is able to generate applications from archetypes and finally, transforms archetypes and data extracts into other EHR standards. We also include in this paper how ResearchEHR has made possible the application of the CEN/ISO 13606 standard in a real environment and the lessons learnt with this experience. © 2011 Elsevier Inc..This work has been partially supported by the Spanish Ministry of Science and Innovation under Grants TIN2010-21388-C02-01 and TIN2010-21388-C02-02, and by the Health Institute Carlos in through the RETICS Combiomed, RD07/0067/2001. Our most sincere thanks to the Hospital of Fuenlabrada in Madrid, including its Medical Director Pablo Serrano together with Marta Terron and Luis Lechuga for their support and work during the development of the medications reconciliation project.Maldonado Segura, JA.; Martínez Costa, C.; Moner Cano, D.; Menárguez-Tortosa, M.; Boscá Tomás, D.; Miñarro Giménez, JA.; Fernández-Breis, JT.... (2012). Using the ResearchEHR platform to facilitate the practical application of the EHR standards. Journal of Biomedical Informatics. 45(4):746-762. doi:10.1016/j.jbi.2011.11.004S74676245

Challenges and opportunities beyond structured data in analysis of electronic health records

Electronic health records (EHR) contain a lot of valuable information about individual patients and the whole population. Besides structured data, unstructured data in EHRs can provide extra, valuable information but the analytics processes are complex, time-consuming, and often require excessive manual effort. Among unstructured data, clinical text and images are the two most popular and important sources of information. Advanced statistical algorithms in natural language processing, machine learning, deep learning, and radiomics have increasingly been used for analyzing clinical text and images. Although there exist many challenges that have not been fully addressed, which can hinder the use of unstructured data, there are clear opportunities for well-designed diagnosis and decision support tools that efficiently incorporate both structured and unstructured data for extracting useful information and provide better outcomes. However, access to clinical data is still very restricted due to data sensitivity and ethical issues. Data quality is also an important challenge in which methods for improving data completeness, conformity and plausibility are needed. Further, generalizing and explaining the result of machine learning models are important problems for healthcare, and these are open challenges. A possible solution to improve data quality and accessibility of unstructured data is developing machine learning methods that can generate clinically relevant synthetic data, and accelerating further research on privacy preserving techniques such as deidentification and pseudonymization of clinical text