Evaluation of Doc’EDS: a French semantic search tool to query health documents from a clinical data warehouse

Pressat-Laffouilhère, Thibaut; Balayé, Pierre; Dahamna, Badisse; Lelong, Romain; Billey, Kévin; Darmoni, Stéfan Jacques; Grosjean, Julien

doi:10.1186/s12911-022-01762-4

Cited by 13 publications

(6 citation statements)

References 19 publications

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“…The conclusion made was that the proposed tool can provide an efficient, effective, and useful method for querying data warehouses and the necessary clinical informatics knowledge. Similarly, in [59], a search tool known as Doc'EDS was proposed to obtain a multilevel search engine combining structured data, clinical narratives, and segmentation. Formal evaluations of the proposed tool semantics features showed excellent results for negation and average results for hypothesis/future.…”

Section: Tools For Clinical Data Managementmentioning

confidence: 99%

“…It offers researchers a means to query structured, unstructured, genomic, and image data contained in a data warehouse. • Doc'EDS Search Tool: Doc'EDS (EDS = Entrepôt de Données de Santé in French, Health Data Warehouse in English), presented in [59] stands as a search tool developed on the clinical data warehouse platform at Rouen University Hospital. This tool serves as a multilevel search engine that effectively combines both structured and unstructured data.…”

Section: Tools For Clinical Data Managementmentioning

confidence: 99%

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A systematic review and meta-data analysis of clinical data repositories in Africa and beyond: recent development, challenges, and future directions

Adewole,

Alozie,

Olagunju

et al. 2024

Discov Data

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A Clinical Data Repository (CDR) is a dynamic database capable of real-time updates with patients' data, organized to facilitate rapid and easy retrieval. CDRs offer numerous benefits, ranging from preserving patients' medical records for follow-up care and prescriptions to enabling the development of intelligent models that can predict, and potentially mitigate serious health conditions. While several research works have attempted to provide state-of-the-art reviews on CDR design and implementation, reviews from 2013 to 2023 cover CDR regulations, guidelines, standards, and challenges in CDR implementation without providing a holistic overview of CDRs. Additionally, these reviews need to adequately address critical aspects of CDR; development and utilization, CDR architecture and metadata, CDR management tools, CDR security, use cases, and artificial intelligence (AI) in CDR design and implementation. The collective knowledge gaps in these works underscore the imperative for a comprehensive overview of the diverse spectrum of CDR as presented in the current study. Existing reviews conducted over the past decade, from 2013 to 2023 have yet to comprehensively cover the critical aspects of CDR development, which are essential for uncovering trends and potential future research directions in Africa and beyond. These aspects include architecture and metadata, security and privacy concerns, tools employed, and more. To bridge this gap, in particular, this study conducts a comprehensive systematic review of CDR, considering critical facets such as architecture and metadata, security and privacy issues, regulations guiding development, practical use cases, tools employed, the role of AI and machine learning (ML) in CDR development, existing CDRs, and challenges faced during CDR development and deployment in Africa and beyond. Specifically, the study extracts valuable discussions and analyses of the different aspects of CDR. Key findings revealed that most architectural models for CDR are still in the theoretical phase, with low awareness and adoption of CDR in healthcare environments, susceptibility to several security threats, and the need to integrate federated learning in CDR systems. Overall, this paper would serve as a valuable reference for designing and implementing cutting-edge clinical data repositories in Africa and beyond.

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Section: Tools For Clinical Data Managementmentioning

confidence: 99%

Section: Tools For Clinical Data Managementmentioning

confidence: 99%

A systematic review and meta-data analysis of clinical data repositories in Africa and beyond: recent development, challenges, and future directions

Adewole,

Alozie,

Olagunju

et al. 2024

Discov Data

View full text Add to dashboard Cite

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“…An automated electronic search algorithm for identifying postoperative complications was evaluated by Tien et al [162]. A semantic health data warehouse was designed to assist health professionals in prescreening eligible patients in clinical trials [163,164]. A combination of structured and unstructured German data was used by Scheurwegs et al [160] to assign clinical codes to patient stays.…”

Section: Classificationmentioning

confidence: 99%

“…Publications, n (%) Data language [15-17,19-25,27-38,41-48,50,51,53-68,71,72,74,75,78-80,83-88,90-92,96,97,99-112,114,116,119-124,126-135, 137,140,142-149,151-154,156-159,161,162,165-176,179,181,184-187,189-192,194-196,198,200-208] 153 (78.9) English [39,49,52,73,76,77,81,89,93,94,113,115,118,125,136,138,139,155,163,164,177,178,182,183,188,193,197] 27 (13.9) French [18,26,69,95,117,150,160,180,199] 9 (4.6) German [40,65,82] 3 (1.5) Korean…”

Section: Referencesmentioning

confidence: 99%

Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review

Bazoge,

Morin,

Daille

et al. 2023

JMIR Med Inform

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Background In recent years, health data collected during the clinical care process have been often repurposed for secondary use through clinical data warehouses (CDWs), which interconnect disparate data from different sources. A large amount of information of high clinical value is stored in unstructured text format. Natural language processing (NLP), which implements algorithms that can operate on massive unstructured textual data, has the potential to structure the data and make clinical information more accessible. Objective The aim of this review was to provide an overview of studies applying NLP to textual data from CDWs. It focuses on identifying the (1) NLP tasks applied to data from CDWs and (2) NLP methods used to tackle these tasks. Methods This review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched for relevant articles in 3 bibliographic databases: PubMed, Google Scholar, and ACL Anthology. We reviewed the titles and abstracts and included articles according to the following inclusion criteria: (1) focus on NLP applied to textual data from CDWs, (2) articles published between 1995 and 2021, and (3) written in English. Results We identified 1353 articles, of which 194 (14.34%) met the inclusion criteria. Among all identified NLP tasks in the included papers, information extraction from clinical text (112/194, 57.7%) and the identification of patients (51/194, 26.3%) were the most frequent tasks. To address the various tasks, symbolic methods were the most common NLP methods (124/232, 53.4%), showing that some tasks can be partially achieved with classical NLP techniques, such as regular expressions or pattern matching that exploit specialized lexica, such as drug lists and terminologies. Machine learning (70/232, 30.2%) and deep learning (38/232, 16.4%) have been increasingly used in recent years, including the most recent approaches based on transformers. NLP methods were mostly applied to English language data (153/194, 78.9%). Conclusions CDWs are central to the secondary use of clinical texts for research purposes. Although the use of NLP on data from CDWs is growing, there remain challenges in this field, especially with regard to languages other than English. Clinical NLP is an effective strategy for accessing, extracting, and transforming data from CDWs. Information retrieved with NLP can assist in clinical research and have an impact on clinical practice.

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“…Clinical Data Warehouses (CDW) are useful to obtain a "real-world picture" of clinicians' use of pharmacogenetic testing [11]. The objective of this study was to evaluate CYP2C19 pharmacogenetic testing and clopidogrel prescription using data from a clinical data warehouse (Entrepôt de Données de Santé Normand EDSaN) [12].…”

Section: Introductionmentioning

confidence: 99%

CYP2C19 Pharmacogenetics and Clopidogrel Prescription in a University Hospital: A Descriptive Study in a Clinical Data Warehouse

Maes,

Gosselin,

Triquenot

et al. 2023

Preprint

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Clopidogrel is one of the most prescribed antiplatelet drugs for cardiovascular diseases prevention. Clopidogrel is a prodrug metabolised by CYP2C19 in active metabolites with anti-aggregant effect. Pharmacogenetic variants such as CYP2C19*2 variant can alter the anti-aggregant effect of clopidogrel leading to a higher risk of cardiovascular disease relapse. The aim of this study was to evaluate the prescription characteristics of CYP2C19 pharmacogenetics in patients on clopidogrel treatment, based on a specific query in a clinical data warehouse. We evaluated the demographics of patients, prescribers, medical indications, CYP2C19 pharmacogenetic tests and therapeutic changes. Neurologists were the main prescribers of CYP2C19 pharmacogenetic tests associated with clopidogrel treatment in a context of suspicion of stroke relapse. Pharmacogenetic tests were mainly prescribed a posteriori to clopidogrel prescription. Clopidogrel pharmacogenetic resistance was associated with the prescription of lysine acetylsalicylate in substitution of clopidogrel. Clear clinical practice guidelines are needed to position CYP2C19 pharmacogenetic testing for patients on clopidogrel treatment.

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Evaluation of Doc’EDS: a French semantic search tool to query health documents from a clinical data warehouse

Cited by 13 publications

References 19 publications

A systematic review and meta-data analysis of clinical data repositories in Africa and beyond: recent development, challenges, and future directions

A systematic review and meta-data analysis of clinical data repositories in Africa and beyond: recent development, challenges, and future directions

Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review

CYP2C19 Pharmacogenetics and Clopidogrel Prescription in a University Hospital: A Descriptive Study in a Clinical Data Warehouse

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