Developing a healthcare dataset information resource (DIR) based on Semantic Web

Abstract

BackgroundThe right dataset is essential to obtain the right insights in data science; therefore, it is important for data scientists to have a good understanding of the availability of relevant datasets as well as the content, structure, and existing analyses of these datasets. While a number of efforts are underway to integrate the large amount and variety of datasets, the lack of an information resource that focuses on specific needs of target users of datasets has existed as a problem for years. To address this gap, we have developed a Dataset Information Resource (DIR), using a user-oriented approach, which gathers relevant dataset knowledge for specific user types. In the present version, we specifically address the challenges of entry-level data scientists in learning to identify, understand, and analyze major datasets in healthcare. We emphasize that the DIR does not contain actual data from the datasets but aims to provide comprehensive knowledge about the datasets and their analyses.MethodsThe DIR leverages Semantic Web technologies and the W3C Dataset Description Profile as the standard for knowledge integration and representation. To extract tailored knowledge for target users, we have developed methods for manual extractions from dataset documentations as well as semi-automatic extractions from related publications, using natural language processing (NLP)-based approaches. A semantic query component is available for knowledge retrieval, and a parameterized question-answering functionality is provided to facilitate the ease of search.ResultsThe DIR prototype is composed of four major components—dataset metadata and related knowledge, search modules, question answering for frequently-asked questions, and blogs. The current implementation includes information on 12 commonly used large and complex healthcare datasets. The initial usage evaluation based on health informatics novices indicates that the DIR is helpful and beginner-friendly.ConclusionsWe have developed a novel user-oriented DIR that provides dataset knowledge specialized for target user groups. Knowledge about datasets is effectively represented in the Semantic Web. At this initial stage, the DIR has already been able to provide sophisticated and relevant knowledge of 12 datasets to help entry health informacians learn healthcare data analysis using suitable datasets. Further development of both content and function levels is underway.

Semantic Web in Healthcare: A Systematic Literature Review of Application, Research Gap, and Future Research Avenues

“…(x) Developed a healthcare dataset information resource (DIR) to hold dataset information and respond to parameterized questions [26].…”

“…A dataset information resource for medical knowledge makes the work more trouble-free and faster. A healthcare dataset information resource has been created along with a question-answering module related to health information [ 26 ]. Combining different databases can be more effective as it expands the information range of knowledge.…”

“…In terms of economic efficiency, the Semantic Web-Based Healthcare Framework (SWBHF) is said to benchmark the existing BioMedLib Search Engine [ 25 ]. SW also offered a new user-oriented dataset information resource (DIR) to boost dataset knowledge and health informatics [ 26 ]. This technology is also used in the rigorous registration process to discover, classify, and composite web services for the service owner [ 4 ].…”

Machine learning in healthcare -- a system's perspective

“…Though there are inklings of AI in medicine, the necessary resources -data -are still lacking. The Dataset Information Resource 1 (DIR) [15] describes over 12 commonly used EHR datasets for research projects, of which only 2 are publicly available [16,17]. The more comprehensive datasets with 100,000+ subjects and integrating healthcare informa-1 https://cci-hit.uncc.edu/dir/index.php/Welcome to DIR tion from diverse care points are proprietary and often require approvals from one or more advisory committees along with access charges [18][19][20][21][22].…”

Mapping progress in intravascular catheter quality surveillance: An Australian case study of electronic medical record data linkage

“…As a result, an increasing number of health professionals and researchers are now leveraging data sets from routine clinical care to improve health outcomes ( 18 ). However, the diversity and complexity of EHR data sets have created challenges, specifically in the collection and comparison of data items for effective analysis of quality and safety queries ( 19 ). Data linkage, interoperability, and heterogenous data items have been flagged as barriers to the implementation of standardized surveillance platforms in many health disciplines.…”