DOE OSTI · 3019961
ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis
Abstract
Objective: Electronic health record (EHR) systems contain a wealth of clinical data stored as both codified data and free-text narrative notes (NLP). The complexity of EHR presents challenges in feature representation, information extraction, and uncertainty quantification. Here, to address these challenges, we proposed an efficient Aggregated naRrative Codified Health (ARCH) records analysis to generate a large-scale knowledge graph (KG) for a comprehensive set of EHR codified and narrative features. Methods: Using data from 12.5 million Veterans Affairs patients, ARCH first derives embedding vectors and generates similarities along with associated p-values to measure the strength of relatedness between clinical features with statistical certainty quantification. Next, ARCH performs a sparse embedding regression to remove indirect linkage between features to build a sparse KG. Finally, ARCH was validated on various clinical tasks, including detecting known relationships between entity pairs, predicting drug side effects, disease phenotyping, as well as sub-typing Alzheimer’s disease patients. Results: ARCH produces high-quality clinical embeddings and KG for over 60,000 codified and narrative EHR concepts. The KG and embeddings are visualized in the R-shiny powered web-API.3 ARCH achieved high accuracy in detecting EHR concept relationships, with AUCs of 0.926 (codified) and 0.861 (NLP) for similar EHR concepts, and 0.810 (codified) and 0.843 (NLP) for related pairs. It detected drug side effects with a 0.723 AUC, which improved to 0.826 after fine-tuning. Using both codified and NLP features, the detection power increased significantly. Compared to other methods, ARCH has superior accuracy and enhances weakly supervised phenotyping algorithms’ performance. Notably, it successfully categorized Alzheimer’s patients into two subgroups with varying mortality rates. Conclusion: The proposed ARCH algorithm generates large-scale high-quality semantic representations and knowledge graph for both codified and NLP EHR features, useful for a wide range of predictive modeling tasks.
Explore related subjects
Keep this discovery
Explore connections, maps & timelines
Gan, Ziming [Univ. of Chicago, IL (United States)], Zhou, Doudou [National Univ. of Singapore (Singapore)] (ORCID:0000000208302287), Rush, Everett [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000256325723), Panickan, Vidul A. [Harvard Medical School, Boston, MA (United States); VA Boston Healthcare System, Boston, MA (United States)], Ho, Yuk-Lam [VA Boston Healthcare System, Boston, MA (United States)] (ORCID:0000000333053830), Ostrouchovm, George [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000220436026), Xu, Zhiwei [Univ. of Michigan, Ann Arbor, MI (United States)], Shen, Shuting [Duke Univ., Durham, NC (United States)], Xiong, Xin [Harvard T.H. Chan School of Public Health, Boston, MA (United States)] (ORCID:0000000211625220), Greco, Kimberly F. [Harvard T.H. Chan School of Public Health, Boston, MA (United States)], Hong, Chuan [Duke Univ., Durham, NC (United States)] (ORCID:0000000170569559), Bonzel, Clara-Lea [Harvard Medical School, Boston, MA (United States); VA Boston Healthcare System, Boston, MA (United States)] (ORCID:0000000152381413), Wen, Jun [Harvard Medical School, Boston, MA (United States)] (ORCID:0000000150672647), Costa, Lauren [VA Boston Healthcare System, Boston, MA (United States)] (ORCID:0000000172375336), Cai, Tianrun [VA Boston Healthcare System, Boston, MA (United States); Brigham and Women's Hospital (Harvard Medical School), Boston, MA (United States)], Begoli, Edmon [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000221733663), Xia, Zongqi [Univ. of Pittsburgh, PA (United States)] (ORCID:0000000315002589), Gaziano, J. Michael [Harvard Medical School, Boston, MA (United States); VA Boston Healthcare System, Boston, MA (United States)], Liao, Katherine P. [VA Boston Healthcare System, Boston, MA (United States); Brigham and Women's Hospital (Harvard Medical School), Boston, MA (United States)] (ORCID:0000000247973200), Cho, Kelly [Harvard Medical School, Boston, MA (United States); VA Boston Healthcare System, Boston, MA (United States)], Cai, Tianxi [Harvard Medical School, Boston, MA (United States); VA Boston Healthcare System, Boston, MA (United States)], Lu, Junwei [Harvard T.H. Chan School of Public Health, Boston, MA (United States); VA Boston Healthcare System, Boston, MA (United States)] (ORCID:0000000337436855). 2025-01-23. ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis. https://doi.org/10.1016/j.jbi.2024.104761
Cite the original work for its findings. Save a collection to share your selection of sources.