The Biomedical Data Translator Program: Conception, Culture, and Community.
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ABSTRACT:
SUBMITTER: Biomedical Data Translator Consortium
PROVIDER: S-EPMC6440573 | biostudies-literature | 2019 Mar
REPOSITORIES: biostudies-literature
Publications
The Biomedical Data Translator Program: Conception, Culture, and Community.
Clinical and translational science 20181109 2
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Project description:Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based "Translator" system capable of integrating existing biomedical data sets and "translating" those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system's architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems.
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Project description:Knowledge-based biomedical data science 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 recent progress in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as progress on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing to construct knowledge graphs, and the expansion of novel knowledge-based approaches to clinical and biological domains.
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Project description:ObjectiveThis study aimed to develop a novel, regulatory-compliant approach for openly exposing integrated clinical and environmental exposures data: the Integrated Clinical and Environmental Exposures Service (ICEES).Materials and methodsThe driving clinical use case for research and development of ICEES was asthma, which is a common disease influenced by hundreds of genes and a plethora of environmental exposures, including exposures to airborne pollutants. We developed a pipeline for integrating clinical data on patients with asthma-like conditions with data on environmental exposures derived from multiple public data sources. The data were integrated at the patient and visit level and used to create de-identified, binned, "integrated feature tables," which were then placed behind an OpenAPI.ResultsOur preliminary evaluation results demonstrate a relationship between exposure to high levels of particulate matter ≤2.5 µm in diameter (PM2.5) and the frequency of emergency department or inpatient visits for respiratory issues. For example, 16.73% of patients with average daily exposure to PM2.5 >9.62 µg/m3 experienced 2 or more emergency department or inpatient visits for respiratory issues in year 2010 compared with 7.93% of patients with lower exposures (n = 23 093).DiscussionThe results validated our overall approach for openly exposing and sharing integrated clinical and environmental exposures data. We plan to iteratively refine and expand ICEES by including additional years of data, feature variables, and disease cohorts.ConclusionsWe believe that ICEES will serve as a regulatory-compliant model and approach for promoting open access to and sharing of integrated clinical and environmental exposures data.
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