Semantic Modeling for Exposomics with Exploratory Evaluation in Clinical Context

Abstract

Exposome is a critical dimension in the precision medicine paradigm. Effective representation of exposomics knowledge is instrumental to melding nongenetic factors into data analytics for clinical research. There is still limited work in (1) modeling exposome entities and relations with proper integration to mainstream ontologies and (2) systematically studying their presence in clinical context. Through selected ontological relations, we developed a template-driven approach to identifying exposome concepts from the Unified Medical Language System (UMLS). The derived concepts were evaluated in terms of literature coverage and the ability to assist in annotating clinical text. The generated semantic model represents rich domain knowledge about exposure events (454 pairs of relations between exposure and outcome). Additionally, a list of 5667 disorder concepts with microbial etiology was created for inferred pathogen exposures. The model consistently covered about 90% of PubMed literature on exposure-induced iatrogenic diseases over 10 years (2001–2010). The model contributed to the efficiency of exposome annotation in clinical text by filtering out 78% of irrelevant machine annotations. Analysis into 50 annotated discharge summaries helped advance our understanding of the exposome information in clinical text. This pilot study demonstrated feasibility of semiautomatically developing a useful semantic resource for exposomics.

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

“…When registry data is not available, claims data may also provide some insight for survival and disease‐related research . Recent work has developed methods for defining the exposome based on clinical narrative information or additional patient‐level measurements . Geo‐coded data can provide a wealth of exposure information including social determinants of health, neighborhood characteristics, socioeconomic status, and pollution information .…”

“…198 Recent work has developed methods for defining the exposome based on clinical narrative information or additional patient-level measurements. 199,200 Geo-coded data can provide a wealth of exposure information including social determinants of health, neighborhood characteristics, socioeconomic status, and pollution information. [201][202][203][204][205][206] Freely available resources like the eICU Collaborative Research Database 207 are becoming more common and increasingly accessible, allowing for additional exploration of data and aggregation for larger analyses.…”

A Narrative Literature Review of Natural Language Processing Applied to the Occupational Exposome

“…This work was subsequently extended by [ 62 ], who are using topic modelling and ontology analysis to provide an updated overview of knowledge representation tools relevant to exposure research. The work by [ 21 ] creates a new semantic resource for exposures, which is evaluated both in a clinical setting and on scientific literature. The resource contains (i) manual annotations derived from clinical notes and knowledge from the Unified Medical Language System (UMLS) to find exposome concepts.…”

The Emerging Landscape of Epidemiological Research Based on Biobanks Linked to Electronic Health Records: Existing Resources, Analytic Challenges and Potential Opportunities

“…214 Recent work has developed methods for defining the exposome based on clinical narrative information in EHRs or based on additional subject-level measurements. 215,216 Geo-coded data can be used of explore a wealth of exposure information including social determinants of health, neighborhood characteristics, socioeconomic status, and pollution information. [217][218][219][220][221][222] In addition to geo-coded and registry data, longitudinal data within the EHR and beyond offers many opportunities for research.…”