Assisting Data Retrieval with a Drug Knowledge Graph

Lelong, Romain; Dahamna, Badisse; Léguillon, R.; Grosjean, Julien; Letord, Catherine; Darmoni, Stéfan Jacques; Soualmia, Lina Fatima

doi:10.3233/shti210909

Cited by 1 publication

(2 citation statements)

References 4 publications

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“…From these data the establishment of clinical data warehouses (CDW) constitutes a key element in optimizing the opportunity for clinical and translational research. 1,2 Generic frameworks and components for CDWs are available, such as I2b2, a data mart used by more than 200 hospitals worldwide. 3,4 However, it does not currently provide the level of detailed analysis necessary for clinical and translational cancer research.…”

Section: Introductionmentioning

confidence: 99%

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Patient Identification and Tumor Identification Management: Quality Program in a Cancer Multicentric Clinical Data Warehouse

Pallier,

Prot,

Naldi

et al. 2023

Cancer Inform

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Background: The Regional Basis of Solid Tumor (RBST), a clinical data warehouse, centralizes information related to cancer patient care in 5 health establishments in 2 French departments. Purpose: To develop algorithms matching heterogeneous data to “real” patients and “real” tumors with respect to patient identification (PI) and tumor identification (TI). Methods: A graph database programed in java Neo4j was used to build the RBST with data from ~20 000 patients. The PI algorithm using the Levenshtein distance was based on the regulatory criteria identifying a patient. A TI algorithm was built on 6 characteristics: tumor location and laterality, date of diagnosis, histology, primary and metastatic status. Given the heterogeneous nature and semantics of the collected data, the creation of repositories (organ, synonym, and histology repositories) was required. The TI algorithm used the Dice coefficient to match tumors. Results: Patients matched if there was complete agreement of the given name, surname, sex, and date/month/year of birth. These parameters were assigned weights of 28%, 28%, 21%, and 23% (with 18% for year, 2.5% for month, and 2.5% for day), respectively. The algorithm had a sensitivity of 99.69% (95% confidence interval [CI] [98.89%, 99.96%]) and a specificity of 100% (95% CI [99.72%, 100%]). The TI algorithm used repositories, weights were assigned to the diagnosis date and associated organ (37.5% and 37.5%, respectively), laterality (16%) histology (5%), and metastatic status (4%). This algorithm had a sensitivity of 71% (95% CI [62.68%, 78.25%]) and a specificity of 100% (95% CI [94.31%, 100%]). Conclusion: The RBST encompasses 2 quality controls: PI and TI. It facilitates the implementation of transversal structuring and assessments of the performance of the provided care.

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Section: Introductionmentioning

confidence: 99%

“…From these data the establishment of clinical data warehouses (CDW) constitutes a key element in optimizing the opportunity for clinical and translational research. 1 , 2 …”

Section: Introductionmentioning

confidence: 99%