Integration of prostate cancer clinical data using an ontology

AbstractIt is increasingly important for investigators to efficiently and effectively access, interpret, and analyze the data from diverse biological, literature, and annotation sources in a unified way. The heterogeneity of biomedical data and the lack of metadata are the primary sources of the difficulty for integration, presenting major challenges to effective search and retrieval of the information. As a proof of concept, the Prostate Cancer Ontology (PCO) is created for the development of the Prostate Cancer Information System (PCIS). PCIS is applied to demonstrate how the ontology is utilized to solve the semantic heterogeneity problem from the integration of two prostate cancer related database systems at the Fox Chase Cancer Center. As the results of the integration process, the semantic query language SPARQL is applied to perform the integrated queries across the two database systems based on PCO

Нечеткие логические выводы и заключения в экспертных системах медицинской диагностики

The main problems in making a correct diagnosis are: subjectivity and insufficient qualifications of the doctor, difficulties in correctly assessing the patient’s complaints, signs and symptoms of the disease observed in the patient, as well as individual manifestations of the symptoms of the disease. In publications on the use of expert systems for medical diagnostics using fuzzy logic, the main attention was paid to the medical features of the problem. In this work, for the first time, general methodological aspects of building such systems, creating databases, representing by fuzzy sets of real numbers, digital scales, linguistic and Boolean data of symptom values are formulated. The types of membership functions that are advisable to use to represent the symptoms of diseases are proposed. In fuzzy-logical conclusions, not only the values of the characteristic functions of the logical terms of individual symptoms, but also complex arithmetic functions of their values are used

Knowledge Guided Integration of Structured and Unstructured Data in Health Decision Process

Data in the health domain is continuously increasing. It is collected from several sources, has several formats and is characterized by its sensibility (protection of personal health data). These characteristics make the management and the expert interaction with the collected data, in order to facilitate decision-making in Health Information Systems (HIS) a challenging field. In this paper, we propose a Knowledge guided integration of structured and unstructured data for health decision process. The knowledge is represented by domain ontology, which allows the integration of structured and unstructured data, stored in NoSQL format. Our motivation is to combine the confirmed advantages of ontologies and NoSQL databases both in data integration and decision aided processes. The proposed ontology has been implemented and evaluated using quality metrics. The approach was evaluated and results show response time optimization, compared with traditional approaches, and improvement of data relevance

Biomedical data integration in computational drug design and bioinformatics

[Abstract In recent years, in the post genomic era, more and more data is being generated by biological high throughput technologies, such as proteomics and transcriptomics. This omics data can be very useful, but the real challenge is to analyze all this data, as a whole, after integrating it. Biomedical data integration enables making queries to different, heterogeneous and distributed biomedical data sources. Data integration solutions can be very useful not only in the context of drug design, but also in biomedical information retrieval, clinical diagnosis, system biology, etc. In this review, we analyze the most common approaches to biomedical data integration, such as federated databases, data warehousing, multi-agent systems and semantic technology, as well as the solutions developed using these approaches in the past few years.Red Gallega de Investigación sobre Cáncer Colorrectal; Ref. 2009/58Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; 209RT- 0366Instituto de Salud Carlos III; PIO52048Instituto de Salud Carlos III; RD07/0067/0005Ministerio de Industria, Turismo y Comercio; TSI-020110-2009-

A Life Cycle Approach to the Development and Validation of an Ontology of the U.S. Common Rule (45 C.F.R. § 46)

Requirements for the protection of human research subjects stem from directly from federal regulation by the Department of Health and Human Services in Title 45 of the Code of Federal Regulations (C.F.R.) part 46. 15 other federal agencies include subpart A of part 46 verbatim in their own body of regulation. Hence 45 C.F.R. part 46 subpart A has come to be called colloquially the ‘Common Rule.’ Overall motivation for this study began as a desire to facilitate the ethical sharing of biospecimen samples from large biospecimen collections by using ontologies. Previous work demonstrated that in general the informed consent process and subsequent decision making about data and specimen release still relies heavily on paper-based informed consent forms and processes. Consequently, well-validated computable models are needed to provide an enhanced foundation for data sharing. This dissertation describes the development and validation of a Common Rule Ontology (CRO), expressed in the OWL-2 Web Ontology Language, and is intended to provide a computable semantic knowledge model for assessing and representing components of the information artifacts of required as part of regulated research under 45 C.F.R. § 46. I examine if the alignment of this ontology with the Basic Formal Ontology and other ontologies from the Open Biomedical Ontology (OBO) Foundry provide a good fit for the regulatory aspects of the Common Rule Ontology. The dissertation also examines and proposes a new method for ongoing evaluation of ontology such as CRO across the ontology development lifecycle and suggest methods to achieve high quality, validated ontologies. While the CRO is not in itself intended to be a complete solution to the data and specimen sharing problems outlined above, it is intended to produce a well-validated computationally grounded framework upon which others can build. This model can be used in future work to build decision support systems to assist Institutional Review Boards (IRBs), regulatory personnel, honest brokers, tissue bank managers, and other individuals in the decision-making process involving biorepository specimen and data sharing

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

Concepção, implementação e validação de um enfoque para integração e recuperação de conhecimento distribuído em bases de dados heterogêneas

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia e Gestão do Conhecimento, Florianópolis, 2010Com o crescimento da demanda e da composição de Bases de Conhecimento para os mais diversos fins e a sua disponibilização através da rede mundial de computadores, passou-se a observar a necessidade de organizar este conhecimento e também integrá-lo para possibilitar maior acessibilidade e facilidade na sua manutenção e utilização, devido à caracterização da disposição dispersa e o formato heterogêneo das referidas bases. Neste trabalho é proposto um sistema que efetua integração do conhecimento de bases de dados em contexto genérico, utilizando como estudo de caso o atendimento emergencial no CIT - Centro de Informações Toxicológicas de Santa Catarina - além de possibilitar a manutenção e manipulação deste artefato através do agrupamento de técnicas de recuperação de informação, aperfeiçoamento semântico, expansão de consulta, fonética em um único mecanismo. Foram avaliadas - através de uma revisão sistemática da literatura - as melhores opções disponibilizadas por estudos prévios em pesquisas realizadas nestas áreas a fim de encontrar a melhor combinação a ser utilizada no mecanismo, além da análise do produto final em um comparativo feito entre mecanismos previamente utilizados pelos profissionais no atendimento de urgência.With growth demand and composition of knowledge bases for different purposes and making them available through internet, it#s possible to see the need to organize this knowledge and also integrate it to provide greater accessibility and ease maintenance and use, due to the characterization of dispersed persistence and format of such heterogeneous databases. This dissertation proposes a system that performs integration of knowledge databases in generic context, using as a case study of emergency care at CIT - Toxicological Information Center of Santa Catarina - besides facilitating the maintenance and manipulation of the artifact by grouping techniques of information retrieval, semantic processing, query expansion, phonetics in a single mechanism. Were evaluated - through a systematic literature review - the best options available in previous studies on research conducted in these areas to find the best combination to be used in the mechanism, besides the analysis of the final product in a comparison made between mechanisms previously used by professionals in emergency care