Cohort Identification Using Semantic Web Technologies: Triplestores as Engines for Complex Computable Phenotyping

Pfaff, Emily R.; Bradford, Robert L.; Clark, Marshall; Balhoff, James P.; Wang, Rujin; Preisser, John S.; Walters, Kellie M; Nielsen, Matthew E.

doi:10.1101/2021.12.02.21267186

Cited by 5 publications

(4 citation statements)

References 27 publications

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“…If we were to follow this reasoning from an a single EAV table to domain-specific tables further, we could also use the data models of HL7 FHIR or OpenEHR itself, as suggested by Paff et al [33]. This would allow to document even more granular data including links between data values.…”

Section: Discussionmentioning

confidence: 99%

Querying a Clinical Data Warehouse for Combinations of Clinical and Imaging Data

Kaspar

Liman²,

Morbach³

et al. 2022

J Digit Imaging

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This study aims to show the feasibility and benefit of single queries in a research data warehouse combining data from a hospital’s clinical and imaging systems. We used a comprehensive integration of a production picture archiving and communication system (PACS) with a clinical data warehouse (CDW) for research to create a system that allows data from both domains to be queried jointly with a single query. To achieve this, we mapped the DICOM information model to the extended entity–attribute–value (EAV) data model of a CDW, which allows data linkage and query constraints on multiple levels: the patient, the encounter, a document, and a group level. Accordingly, we have integrated DICOM metadata directly into CDW and linked it to existing clinical data. We included data collected in 2016 and 2017 from the Department of Internal Medicine in this analysis for two query inquiries from researchers targeting research about a disease and in radiology. We obtained quantitative information about the current availability of combinations of clinical and imaging data using a single multilevel query compiled for each query inquiry. We compared these multilevel query results to results that linked data at a single level, resulting in a quantitative representation of results that was up to 112% and 573% higher. An EAV data model can be extended to store data from clinical systems and PACS on multiple levels to enable combined querying with a single query to quickly display actual frequency data.

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Section: Discussionmentioning

confidence: 99%

Querying a Clinical Data Warehouse for Combinations of Clinical and Imaging Data

Kaspar

Liman²,

Morbach³

et al. 2022

J Digit Imaging

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“…Embedding of the CP within a knowledge graph, a semantic web-based model for representing interconnected data, 48 would enhance interoperability across sites, where underlying schemas differ. 49 This CP for identifying relapse retrospectively demonstrates strong performance using objective, readily accessible registry data. Our electronic algorithm can be used by researchers to calculate the individualised probability of relapse, hence ensuring more accurate outcome ascertainment in real-world research in AAV, where BVAS may be incomplete or inaccurate.…”

Section: Vasculitis Vasculitis Vasculitismentioning

confidence: 99%

Computable phenotype for real-world, data-driven retrospective identification of relapse in ANCA-associated vasculitis

Scott,

White,

Walsh

et al. 2024

RMD Open

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ObjectiveANCA-associated vasculitis (AAV) is a relapsing-remitting disease, resulting in incremental tissue injury. The gold-standard relapse definition (Birmingham Vasculitis Activity Score, BVAS>0) is often missing or inaccurate in registry settings, leading to errors in ascertainment of this key outcome. We sought to create a computable phenotype (CP) to automate retrospective identification of relapse using real-world data in the research setting.MethodsWe studied 536 patients with AAV and >6 months follow-up recruited to the Rare Kidney Disease registry (a national longitudinal, multicentre cohort study). We followed five steps: (1) independent encounter adjudication using primary medical records to assign the ground truth, (2) selection of data elements (DEs), (3) CP development using multilevel regression modelling, (4) internal validation and (5) development of additional models to handle missingness. Cut-points were determined by maximising the F1-score. We developed a web application for CP implementation, which outputs an individualised probability of relapse.ResultsDevelopment and validation datasets comprised 1209 and 377 encounters, respectively. After classifying encounters with diagnostic histopathology as relapse, we identified five key DEs; DE1: change in ANCA level, DE2: suggestive blood/urine tests, DE3: suggestive imaging, DE4: immunosuppression status, DE5: immunosuppression change. F1-score, sensitivity and specificity were 0.85 (95% CI 0.77 to 0.92), 0.89 (95% CI 0.80 to 0.99) and 0.96 (95% CI 0.93 to 0.99), respectively. Where DE5 was missing, DE2 plus either DE1/DE3 were required to match the accuracy of BVAS.ConclusionsThis CP accurately quantifies the individualised probability of relapse in AAV retrospectively, using objective, readily accessible registry data. This framework could be leveraged for other outcomes and relapsing diseases.

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“…This issue is further complicated by the utilization of several medical ontologies to generalize the data (for example, SNOMED-CT, UMLS, ICD-9, and ICD-10). These ontologies introduce conflicts and inconsistencies, further complicating the situation [7]. In addition, instructional and case management support tools need to be built to ensure that complete and evidence-based information provided by machine learning technology is actionable for every patient.…”

Section: Introductionmentioning

confidence: 99%

Current State of Artificial Intelligence (AI) in Oncology: A Review

Ali

NAEEM²,

Anam

et al. 2023

CTO

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Artificial intelligence (AI) has made significant strides in recent years toward resolving a wide range of biological issues, including a number of occurrences connected to cancer. Deep learning is an adaptable sector of AI that enables the automatic extraction of features. It is increasingly being used in various fields of cancer research, both scientific and clinical. In this study, several current applications of AI in oncology, including the situations in which deep learning has effectively addressed intractable disputes, are discussed. This study also discusses the challenges that need to be surmounted before such an application of AI may be implemented more broadly. Similarly, it brings to light resources and data sets that can assist in maximizing the potential of AI. Essential insights in oncology may be generated based on the development of novel methodologies and uses of AI, making it possible for substantial changes to be made to clinical practice.

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Cohort Identification Using Semantic Web Technologies: Triplestores as Engines for Complex Computable Phenotyping

Cited by 5 publications

References 27 publications

Querying a Clinical Data Warehouse for Combinations of Clinical and Imaging Data

Querying a Clinical Data Warehouse for Combinations of Clinical and Imaging Data

Computable phenotype for real-world, data-driven retrospective identification of relapse in ANCA-associated vasculitis

Current State of Artificial Intelligence (AI) in Oncology: A Review

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