Web-based interactive mapping from data dictionaries to ontologies, with an application to cancer registry

Abstract

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.

Machine and cognitive intelligence for human health: systematic review

“…In terms of additional information use, future studies can consider additional ontologies, features, new data sources and features, demographic information, additional types of mappings or services, and multiple context information. First, Falkman et al [ 34 ] mentioned exploiting semantic Web-based foundation by using domain ontology and reasoning and by adding user and organizational ontologies; Tao et al [ 88 ] highlighted the need to allow ontology import in the Web ontology language; and Traverso et al [ 14 ] indicated using radiation oncology ontology “combined with other ontologies under development to combine and link DICOM information, clinical data and quantitative features computed on patients’ images and variables (p. 861)”. Second, Kim et al [ 73 ] suggested identifying and using additional features or entities (e.g., diseases, places, and time) that are important for determining “whether a report mentions an infectious disease outbreak (p. 12)” in deep learning models; Peral et al [ 89 ] indicated including new data sources like social networks; Chen et al [ 50 ] highlighted adding new features to facilitate annotating nodules on computed tomography images; Motlagh et al [ 53 ] mentioned adding features like NLP and the provision of consultancy services, psychotherapy, and medication.…”

Strategies and Techniques for Quality Control and Semantic Enrichment with Multimodal Data: A Case Study in Colorectal Cancer with eHDPrep

“…The results of semantic enrichment in eHDPrep are critically dependent upon the ontology taken as input, and will likely suffer from a degree of annotation bias [76]. Also, mapping variables to ontology terms can be time-consuming and may require background knowledge of the variables if their labels are not self-explanatory; these issues may be mitigated by fuzzy string matching [77] and software interfaces, for example the UK National Health Service Digital SNOMED CT Browser [38]. Importantly, variables generated through semantic enrichment might not properly represent the quantitative relationship between their constituent variables due to unusual associations, such as J-curves [78].…”

Ontologies and Knowledge Graphs in Oncology Research

“…This annotated value can then be further integrated into developing solutions and their overall context. In addition, ontologies can be directly used as vocabularies to support the organization of data according to known domain information [12]. One objective for this use is, for instance, allowing users to search data that has been annotated using ontologies in a database.…”

“…Li et al [72], on the other hand, constructed a KG by first extracting knowledge triples from available data and then using these to construct a network for healthcare professionals that allows them to traverse this contextualized knowledge. Tao et al [12] developed a web-based system called Interactive Mapping Interface (IMI) to first map the data dictionary in use by the North American Association of Central Cancer Registries (NAACCR) to the NCIt with the final goal of facilitating the dissemination and reuse of North American cancer registries data. Chen et al [73] established a consensus knowledge for cancer hallmarks using functional annotations and gene set overlap, again aiming towards enabling the ability to compare data from different sources.…”