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Léguillon R, Gosselin L, Carnoy C, Pressat-Laffouilhere T, Letord C, Dahamna B, Darmoni SJ, Grosjean J. Integrating a new knowledge organisation system for monoclonal antibodies for therapeutic use authorised in Europe into HeTOP terminology-ontology server. J Biomed Inform 2023; 140:104325. [PMID: 36870586 DOI: 10.1016/j.jbi.2023.104325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 02/06/2023] [Accepted: 02/25/2023] [Indexed: 03/06/2023]
Abstract
Monoclonal antibodies (MAs) are increasingly used in the therapeutic arsenal. Clinical Data Warehouses (CDWs) offer unprecedented opportunities for research on real-word data. The objective of this work is to develop a knowledge organization system on MAs for therapeutic use (MATUs) applicable in Europe to query CDWs from a multi-terminology server (HeTOP). After expert consensus, three main health thesauri were selected: the MeSH thesaurus, the National Cancer Institute thesaurus (NCIt) and the SNOMED CT. These thesauri contain 1,723 MAs concepts, but only 99 (5.7 %) are identified as MATUs. The knowledge organisation system proposed in this article is a six-level hierarchical system according to their main therapeutic target. It includes 193 different concepts organised in a cross lingual terminology server, which will allow the inclusion of semantic extensions. Ninety nine (51.3 %) MATUs concepts and 94 (48.7 %) hierarchical concepts composed the knowledge organisation system. Two separates groups (an expert group and a validation group) carried out the selection, creation and validation processes. Queries identify, for unstructured data, 83 out of 99 (83.8 %) MATUs corresponding to 45,262 patients, 347,035 hospital stays and 427,544 health documents, and for structured data, 61 out of 99 (61.6 %) MATUs corresponding to 9,218 patients, 59,643 hospital stays and 104,737 hospital prescriptions. The volume of data in the CDW demonstrated the potential for using these data in clinical research, although not all MATUs are present in the CDW (16 missing for unstructured data and 38 for structured data). The knowledge organisation system proposed here improves the understanding of MATUs, the quality of queries and helps clinical researchers retrieve relevant medical information. The use of this model in CDW allows for the rapid identification of a large number of patients and health documents, either directly by a MATU of interest (e.g. Rituximab) but also by searching for parent concepts (e.g. Anti-CD20 Monoclonal Antibody).
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Affiliation(s)
- Romain Léguillon
- Department of Digital Health, Rouen University Hospital, Rouen, France; Department of Pharmacy, Rouen University Hospital, Rouen, France; Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé (LIMICS), U1142, INSERM, Sorbonne Université, Paris, France.
| | - Laura Gosselin
- Department of Digital Health, Rouen University Hospital, Rouen, France; Department of Pharmacy, Rouen University Hospital, Rouen, France
| | - Christophe Carnoy
- Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, U1019 - UMR 9017 - CIIL - Center for Infection and Immunity of Lille, F-59000 Lille, France; GIVRE, Univ-Lille, France
| | - Thibaut Pressat-Laffouilhere
- Clinique Ambroise Paré, groupe ELSAN Department of medical information, 387 Rte de Saint-Simon, F-31100 Toulouse, France
| | - Catherine Letord
- Department of Digital Health, Rouen University Hospital, Rouen, France; Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé (LIMICS), U1142, INSERM, Sorbonne Université, Paris, France
| | - Badisse Dahamna
- Department of Digital Health, Rouen University Hospital, Rouen, France; Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé (LIMICS), U1142, INSERM, Sorbonne Université, Paris, France
| | - Stéfan J Darmoni
- Department of Digital Health, Rouen University Hospital, Rouen, France; Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé (LIMICS), U1142, INSERM, Sorbonne Université, Paris, France
| | - Julien Grosjean
- Department of Digital Health, Rouen University Hospital, Rouen, France; Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé (LIMICS), U1142, INSERM, Sorbonne Université, Paris, France
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Pressat-Laffouilhère T, Balayé P, Dahamna B, Lelong R, Billey K, Darmoni SJ, Grosjean J. Evaluation of Doc'EDS: a French semantic search tool to query health documents from a clinical data warehouse. BMC Med Inform Decis Mak 2022; 22:34. [PMID: 35135538 PMCID: PMC8822768 DOI: 10.1186/s12911-022-01762-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 01/20/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Unstructured data from electronic health records represent a wealth of information. Doc'EDS is a pre-screening tool based on textual and semantic analysis. The Doc'EDS system provides a graphic user interface to search documents in French. The aim of this study was to present the Doc'EDS tool and to provide a formal evaluation of its semantic features. METHODS Doc'EDS is a search tool built on top of the clinical data warehouse developed at Rouen University Hospital. This tool is a multilevel search engine combining structured and unstructured data. It also provides basic analytical features and semantic utilities. A formal evaluation was conducted to measure the impact of Natural Language Processing algorithms. RESULTS Approximately 18.1 million narrative documents are stored in Doc'EDS. The formal evaluation was conducted in 5000 clinical concepts that were manually collected. The F-measures of negative concepts and hypothetical concepts were respectively 0.89 and 0.57. CONCLUSION In this formal evaluation, we have shown that Doc'EDS is able to deal with language subtleties to enhance an advanced full text search in French health documents. The Doc'EDS tool is currently used on a daily basis to help researchers to identify patient cohorts thanks to unstructured data.
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Affiliation(s)
- Thibaut Pressat-Laffouilhère
- Department of Biomedical Informatics, Rouen University Hospital, Normandy, France.,LITIS EA4108, Rouen University, Normandy, France
| | - Pierre Balayé
- Department of Biomedical Informatics, Rouen University Hospital, Normandy, France
| | - Badisse Dahamna
- Department of Biomedical Informatics, Rouen University Hospital, Normandy, France.,LIMICS U1142 INSERM, Sorbonne Université & Sorbonne Paris Nord, Paris, France
| | - Romain Lelong
- Department of Biomedical Informatics, Rouen University Hospital, Normandy, France.,LIMICS U1142 INSERM, Sorbonne Université & Sorbonne Paris Nord, Paris, France
| | - Kévin Billey
- Department of Biomedical Informatics, Rouen University Hospital, Normandy, France.,LITIS EA4108, Rouen University, Normandy, France
| | - Stéfan J Darmoni
- Department of Biomedical Informatics, Rouen University Hospital, Normandy, France.,LIMICS U1142 INSERM, Sorbonne Université & Sorbonne Paris Nord, Paris, France
| | - Julien Grosjean
- Department of Biomedical Informatics, Rouen University Hospital, Normandy, France. .,LIMICS U1142 INSERM, Sorbonne Université & Sorbonne Paris Nord, Paris, France.
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Kim KH, Park JH, Ro YS, Hong KJ, Song KJ, Shin SD. Emergency department routine data and the diagnosis of acute ischemic heart disease in patients with atypical chest pain. PLoS One 2020; 15:e0241920. [PMID: 33152007 PMCID: PMC7644067 DOI: 10.1371/journal.pone.0241920] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 10/22/2020] [Indexed: 11/21/2022] Open
Abstract
Background Due to an aging population and the increasing proportion of patients with various comorbidities, the number of patients with acute ischemic heart disease (AIHD) who present to the emergency department (ED) with atypical chest pain is increasing. The aim of this study was to develop and validate a prediction model for AIHD in patients with atypical chest pain. Methods and results A chest pain workup registry, ED administrative database, and clinical data warehouse database were analyzed and integrated by using nonidentifiable key factors to create a comprehensive clinical dataset in a single academic ED from 2014 to 2018. Demographic findings, vital signs, and routine laboratory test results were assessed for their ability to predict AIHD. An extreme gradient boosting (XGB) model was developed and evaluated, and its performance was compared to that of a single-variable model and logistic regression model. The area under the receiver operating characteristic curve (AUROC) was calculated to assess discrimination. A calibration plot and partial dependence plots were also used in the analyses. Overall, 4,978 patients were analyzed. Of the 3,833 patients in the training cohort, 453 (11.8%) had AIHD; of the 1,145 patients in the validation cohort, 166 (14.5%) had AIHD. XGB, troponin (single-variable), and logistic regression models showed similar discrimination power (AUROC [95% confidence interval]: XGB model, 0.75 [0.71–0.79]; troponin model, 0.73 [0.69–0.77]; logistic regression model, 0.73 [0.70–0.79]). Most patients were classified as non-AIHD; calibration was good in patients with a low predicted probability of AIHD in all prediction models. Unlike in the logistic regression model, a nonlinear relationship-like threshold and U-shaped relationship between variables and the probability of AIHD were revealed in the XGB model. Conclusion We developed and validated an AIHD prediction model for patients with atypical chest pain by using an XGB model.
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Affiliation(s)
- Ki Hong Kim
- Department of Emergency Medicine, Seoul National University Hospital, Seoul, Korea
- Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul, Korea
| | - Jeong Ho Park
- Department of Emergency Medicine, Seoul National University Hospital, Seoul, Korea
- Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul, Korea
- * E-mail:
| | - Young Sun Ro
- Department of Emergency Medicine, Seoul National University Hospital, Seoul, Korea
- Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul, Korea
| | - Ki Jeong Hong
- Department of Emergency Medicine, Seoul National University Hospital, Seoul, Korea
- Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul, Korea
| | - Kyoung Jun Song
- Department of Emergency Medicine, Seoul National University Hospital, Seoul, Korea
- Department of Emergency Medicine, Seoul National University Boramae Medical Center, Seoul, Korea
| | - Sang Do Shin
- Department of Emergency Medicine, Seoul National University Hospital, Seoul, Korea
- Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul, Korea
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