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Expanding the OMOP common data model to Support Perinatal Research in Network Studies

PHARMACOEPIDEMIOLOGY AND DRUG SAFETY 2025年第2期34卷 e70106页

作者： Abellan, Alicia Burn, Edward Trinh, Nhung T. H. Burkard, Theresa Callahan, Alison Fernandez-Bertolin, Sergio Hurley, Eimir Rodriguez, Clara Segundo, Elena Morales, Daniel R. Nordeng, Hedvig M. E. Duarte-Salles, Talita Fundacio Inst Univ Recerca Atencio Primaria Salut Barcelona Spain Univ Oxford Ctr Stat Med CSM Nuffield Dept Orthopaed Rheumatol & Musculoskeleta Oxford England Univ Oslo Fac Math & Nat Sci Dept Pharm & PharmaTox Strateg Res Initiat PharmacoEpidemiol & Drug Safety Res Grp Oslo Norway Stanford Univ Ctr Biomed Informat Res Sch Med Stanford CA USA Univ Dundee Div Populat Hlth & Genom Dundee Scotland Norwegian Inst Publ Hlth Dept Child Hlth & Dev Oslo Norway Erasmus MC Dept Med Informat Rotterdam Netherlands

ObjectivesThe Observational Medical Outcomes Partnership common data model (OMOP-CDM) is a useful tool for large-scale network analysis but currently lacks a structured approach to pregnancy episodes. We aimed to develop and implement a perinatal expansion for the OMOP-CDM to facilitate perinatal network *** collaboratively developed a perinatal expansion with input from domain experts and stakeholders to reach consensus. The structure and vocabularies followed the OMOP-CDM ontological framework principles. We tested the expansion using SIDIAP and Norwegian databases. We developed a diagnostics package for quality control assessment and conducted a descriptive analysis on the captured perinatal data mapped to the *** perinatal expansion consists of a pregnancy table and an infant table, each with required and optional variables incorporated into standardized vocabularies. Quality assessment of the perinatal expansion table in SIDIAP and Norwegian databases demonstrated accurate capture of perinatal characteristics. Descriptive analysis measured the number of pregnancies (SIDIAP: 646 530;Norway: 746 671), pregnancy outcomes (e.g., 0.5% stillbirths in SIDIAP and 0.4% in Norway), gestational length (median [IQR] in days, SIDIAP: 273 [56-280];Norway: 280 [273-286]), number of infants (Norway: 758 806), and birth weight (median [IQR] in grams, Norway: 3520 [3175-3860)], among other relevant *** and ConclusionWe developed and implemented a perinatal expansion that captures important variables for perinatal research and allows interoperability with existing tables in the OMOP-CDM, which is expected to facilitate future network studies. The publicly available diagnostics package enables testing the implementation of the extension table and the quality and completeness of available data on pregnancy and pregnancy-related outcomes in databases mapped to the OMOP CDM.

关键词： common data model medical ontologies OMOP perinatal epidemiology pregnancy

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Implementing a common data model in Ophthalmology: Mapping Structured Electronic Health Record Ophthalmic Examination data to Standard Vocabularies

OPHTHALMOLOGY SCIENCE

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OPHTHALMOLOGY SCIENCE 2025年第2期5卷 100666-100666页

作者： Quon, Justin C. Long, Christopher P. Halfpenny, William Chuang, Amy Cai, Cindy X. Baxter, Sally L. Daketi, Vamsi Schmitz, Amanda Bahroos, Neil Xu, Benjamin Y. Toy, Brian C. Univ Southern Calif Los Angeles Roski Eye Inst Keck Sch Med Dept Ophthalmol Los Angeles CA USA Univ Calif San Diego Viterbi Family Dept Ophthalmol Div Ophthalmol Informat & Data Sci La Jolla CA USA Univ Calif San Diego Shiley Eye Inst La Jolla CA USA Southern Calif Clin & Translat Sci Inst Los Angeles CA USA Johns Hopkins Sch Med Wilmer Eye Inst Baltimore MD USA USC Dept Informat Technol Keck Med Los Angeles CA USA

Objective: To identify and characterize concept coverage gaps of ophthalmology examination data elements within the Cerner Millennium electronic health record (EHR) implementations by the Observational Health data Sciences and Informatics Observational Medical Outcomes Partnership (OMOP) common data model (CDM). Design: Analysis of data elements in EHRs. Subjects: Not applicable. Methods: Source eye examination data elements from the default Cerner model Experience EHR and a local implementation of the Cerner Millennium EHR were extracted, classified into one of 8 subject categories, and mapped to the semantically closest standard concept in the OMOP CDM. Mappings were categorized as exact, if the data element and OMOP concept represented equivalent information, wider, if the OMOP concept was missing conceptual granularity, narrower, if the OMOP concept introduced excess information, and unmatched, if no standard concept adequately represented the data element. Descriptive statistics and qualitative analysis were used to describe the concept coverage for each subject category. Main Outcome Measures: Concept coverage gaps in 8 ophthalmology subject categories of data elements by the OMOP CDM. Results: There were 409 and 947 ophthalmology data elements in the default and local Cerner modules, respectively. Of the 409 mappings in the default Cerner module, 25% (n = 102) were exact, 53% (n = 217) were wider, 3% (n = 11) were narrower, and 19% (n = 79) were unmatched. In the local Cerner module, 18% (n = 173) of mappings were exact, 54% (n = 514) were wider, 1% (n = 10) were narrower, and 26% (n = 250) were unmatched. The largest coverage gaps were seen in the local Cerner module under the visual acuity, sensorimotor testing, and refraction categories, with 95%, 95%, and 81% of data elements in each respective category having mappings that were not exact. Concept coverage gaps spanned all 8 categories in both EHR implementations. Conclusions: Considerable coverage gaps b

关键词： common data model data standards Electronic health record OHDSI OMOP CDM common data model EHR electronic health record ETL extract-transform-load IOP intraocular pressure logMAR logarithm of the minimum angle of resolution LOINC Logical Observation Identifiers, Names, and Codes OD oculus dexter OMOP Observational Medical Outcomes Partnership OS oculus sinister OU oculus uterque SNOMED Systematized Nomenclature of Medicine VA visual acuity

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Conversion of Sensitive data to the Observational Medical Outcomes Partnership common data model: Protocol for the Development and Use of Carrot

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JMIR RESEARCH PROTOCOLS 2025年 14卷 e60917页

作者： Cox, Samuel Masood, Erum Panagi, Vasiliki Macdonald, Calum Milligan, Gordon Horban, Scott Santos, Roberto Hall, Chris Lea, Daniel Tarr, Simon Mumtaz, Shahzad Akashili, Emeka Rae, Andy Urwin, Esmond Cole, Christian Sheikh, Aziz Jefferson, Emily Quinlan, Philip Roy Univ Nottingham Digital Res Serv Nottingham England Univ Dundee Hlth Informat Ctr Dundee Scotland Univ Edinburgh Edinburgh Scotland Univ Nottingham NIHR Nottingham Biomed Res Ctr Sch Med Nottingham England Univ Oxford Nuffield Dept Primary Care Hlth Sci Oxford England

Background: The use of data standards is low across the health care system, and converting data to a common data model (CDM) is usually required to undertake international *** is the Observational Medical Outcomes Partnership (OMOP) CDM. It has gained substantial traction across researchers and those who have developed data platforms. The Observational Health Care data Sciences and Informatics (OHDSI) partnership manages OMOP and provides many open-source tools to assist in converting data to the OMOP CDM. The challenge, however, is in the skills, knowledge, know-how, and capacity within teams to convert their data to OMOP. The European Health Care data Evidence Network provided funds to allow data owners to bring in external resources to do the required conversions. The Carrot software (University of Nottingham) is a new set of open-source tools designed to help address these challenges while not requiring data access by external resources. Objective: The use of data protection rules is increasing, and privacy by design is a core principle under the European and UK legislations related to data protection. Our aims for the Carrot software were to have a standardized mechanism for managing the data curation process, capturing the rules used to convert the data, and creating a platform that can reuse rules across projects to drive standardization of process and improve the speed without compromising on quality. Most importantly, we aimed to deliver this design-by-privacy approach without requiring data access to those creating the rules. Methods: The software was developed using Agile approaches by both software engineers and data engineers, who would ultimately use the system. Experts in OMOP were used to ensure the approaches were correct. An incremental release program was initiated to ensure we delivered continuous progress. Results: Carrot has been delivered and used on a project called COVID-Curated and Open Analysis and Research Platform (CO-C

关键词： data standardization OMOP Observational Medical OutcomesPartnership ETL extract, transform, and load data discovery transparency Carrot tool common data model data standard health care data model data protection data privacy open-source

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Predicting the likelihood of readmission in patients with ischemic stroke: An explainable machine learning approach using common data model data

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INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS 2025年 195卷 105754-105754页

作者： Hwang, Yu Seong Kim, Seongheon Yim, Inhyeok Park, Yukyoung Kang, Seonguk Jo, Heui Sug Kangwon Natl Univ Sch Med Dept Hlth Policy & Management 510 Sch Med Bldg 1 N4141 Kangwondaehak Gil Chuncheon Si 24341 Gangwon Do South Korea Kangwon Natl Univ Hosp Dept Neurol 156 Baengnyeong Ro Chuncheon Si 24289 Gangwon Do South Korea Kangwon Natl Univ Kangwon Natl Univ Hosp Sch Med Dept Family Med 156 Baengnyeong Ro Chuncheon Si 24289 Gangwon Do South Korea Kangwon Natl Univ Hosp Dept Prevent Med 156 Baengnyeong Ro Chuncheon Si 24289 Gangwon Do South Korea Kangwon Natl Univ Hosp Dept Convergence Secur 156 Baengnyeong Ro Chuncheon Si 24289 Gangwon Do South Korea Gangwon State Res Inst Peoples Hlth Team Publ Med Policy Dev 880 Baksa Ro Chuncheon Si 24461 Gangwon Do South Korea

Background: Ischemic stroke affects 15 million people worldwide, causing five million deaths annually. Despite declining mortality rates, stroke incidence and readmission risks remain high, highlighting the need for preventing readmission to improve the quality of life of survivors. This study developed a machine-learning model to predict 90-day stroke readmission using electronic medical records converted to the common data model (CDM) from the Regional Accountable Care Hospital in Gangwon state in South Korea. Methods: We retrospectively analyzed data from 1,136 patients with ischemic stroke admitted between August 2003 and August 2021 after excluding cases with missing blood test values. Demographics, blood test results, treatments, and comorbidities were used as key features. Six machine learning models and three deep learning models were used to predict 90-day readmission using the synthetic minority over-sampling technique to address class imbalance. models were evaluated using threefold cross-validation, and SHapley Additive exPlanations (SHAP) values were calculated to interpret feature importance. Results: Among 1,136 patients, 196 (17.2 %) were readmitted within 90 days. Male patients were significantly more likely to experience readmission (p = 0.02). LightGBM achieved an area under the curve of 0.94, demonstrating that analyzing stroke and stroke-related conditions provides greater predictive accuracy than predicting stroke alone or all-cause readmissions. SHAP analysis highlighted renal and metabolic variables, including creatinine, blood urea nitrogen, calcium, sodium, and potassium, as key predictors of readmission. Conclusion: Machine-learning models using electronic health record-based CDM data demonstrated strong predictive performance for 90-day stroke readmission. These results support personalized post-discharge management and lay the groundwork for future multicenter studies.

关键词： Stroke Readmission common data model Machine learning SHAP

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A common data model for oral anticoagulants-related risk of spontaneous intracranial hemorrhage

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JOURNAL OF CLINICAL NEUROSCIENCE 2025年 133卷 111039-111039页

作者： Hong, Noah Ko, Yeh-Hee Park, Jeong Hyun Ha, Eun Jin Lee, Sung Ho Kim, Kang Min Kang, Hyun-Seung Kim, Jeong Eun Kim, Kwangsoo Cho, Won-Sang Seoul Natl Univ Boramae Med Ctr Seoul Metropolitan Govt Dept Neurosurg 20 Boramae Ro 5 Gil Seoul 07061 South Korea Yonsei Univ Dept Appl Stat 50 Yonsei Ro Seoul 03722 South Korea Seoul Natl Univ Hosp Biomed Res Inst 101 DeaHak Ro Seoul 03080 South Korea NAVER Corp 95 Jeongjail Ro Seongnam Si Gyeonggi South Korea Seoul Natl Univ Hosp Dept Crit Care Med 101 Daehak Ro Seoul 03080 South Korea Seoul Natl Univ Hosp Transdisciplinary Dept Med & Adv Technol 101 Daehak ro Seoul 03080 South Korea Seoul Natl Univ Seoul Natl Univ Hosp Coll Med Dept Neurosurg 101 Daehak Ro Seoul 03080 South Korea

Spontaneous intracranial hemorrhage (sICH) is a major complication associated with oral anticoagulation which results in a high mortality rate, and the incidence of anticoagulant-induced sICH has increased markedly, so it is necessary to investigate the risk of anticoagulation-related sICH in a real-world setting. We aimed to investigate the incidence and risk factors of oral anticoagulant-related sICH using a common data model (CDM), and to determine whether a clinical study using the CDM would be comparable to conventional studies. After converting the various clinical codes of 12,821 patients taking oral anticoagulants, such as warfarin and non-vitamin K antagonist oral anticoagulants (NOACs), into the Observational Medical Outcomes Partnership (OMOP) CDM format, we analyzed the incidence and risk factors of sICH. sICH occurred in 0.5 % of 5,626 patients with warfarin and 0.2 % of 7,195 patients with NOAC. The mean duration of warfarin and NOACs before sICH occurrence was 251.4 +/- 373.6 and 124.2 +/- 135.7 days, respectively. Multivariable analysis showed significant risk factors of the sICH, such as warfarin over NOACs;hypertension;diabetes mellitus;brain tumors;and decreased duration of oral anticoagulation. NOACs demonstrated a lower risk of sICH than warfarin in a real- world setting using OMOP CDM confined to a single institution. Clinical studies using a CDM for the multicenter datasets may provide more reliable information about the risk of sICH.

关键词： Warfarin Non-vitamin K antagonist oral anticoagulants Risk factors Spontaneous intracranial hemorrhage common data model

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Machine Learning for Selecting Important Clinical Markers of Imaging Subgroups of Cerebral Small Vessel Disease Based on a common data model

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Tsinghua Science and Technology 2024年第5期29卷 1495-1508页

作者： Lan Lan Guoliang Hu Rui Li Tingting Wang Lingling Jiang Jiawei Luo Zhiwei Ji Yilong Wang IT Center Beijing Tiantan HospitalCapital Medical UniversityBeijing 100070China Department of Neurology Beijing Tiantan HospitalCapital Medical UniversityBeijing 100070China China National Clinical Research Center for Neurological Diseases Beijing Tiantan HospitalCapital Medical UniversityBeijing 100070China West China Biomedical Big Data Center West China HospitalSichuan UniversityChengdu 610044China College of Artificial Intelligence Nanjing Agricultural UniversityNanjing 210095China

Differences in the imaging subgroups of cerebral small vessel disease(CSVD)need to be further ***,we use propensity score matching to obtain balanced *** random forest(RF)is adopted to classify the subgroups compared with support vector machine(SVM)and extreme gradient boosting(XGBoost),and to select the *** top 10 important features are included in the stepwise logistic regression,and the odds ratio(OR)and 95%confidence interval(CI)are *** are 41290 adult inpatient records diagnosed with *** and area under curve(AUC)of RF are close to 0.7,which performs best in classification compared to SVM and *** and 95%CI of hematocrit for white matter lesions(WMLs),lacunes,microbleeds,atrophy,and enlarged perivascular space(EPVS)are 0.9875(0.9857−0.9893),0.9728(0.9705−0.9752),0.9782(0.9740−0.9824),1.0093(1.0081−1.0106),and 0.9716(0.9597−0.9832).OR and 95%CI of red cell distribution width for WMLs,lacunes,atrophy,and EPVS are 0.9600(0.9538−0.9662),0.9630(0.9559−0.9702),1.0751(1.0686−1.0817),and 0.9304(0.8864−0.9755).OR and 95%CI of platelet distribution width for WMLs,lacunes,and microbleeds are 1.1796(1.1636−1.1958),1.1663(1.1476−1.1853),and 1.0416(1.0152−1.0687).This study proposes a new analytical framework to select important clinical markers for CSVD with machine learning based on a common data model,which has low cost,fast speed,large sample size,and continuous data sources.

关键词： common data model machine learning cerebral small vessel disease imaging subgroups clinical markers

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common data model for Healthcare data 9

Common Data Model for Healthcare data

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9th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

作者： Khan, Umair M. Kothari, Huzaifa Kuchekar, Aditya Koshy, Reeta Bharatiya Vidya Bhavans Sardar Patel Inst Technol Dept Comp Engn Bombay Maharashtra India

ISBN: (纸本)9781538644300

There is a huge volume of data made openly available to the masses on Government data sites such as the ***. However, it is very difficult to make sense between data from two different data sets or different domains. As a result a lot of time is spent in just collecting and linking this data from various sources before it can be put to use. A potential solution to this problem is using a common data model. A common data model is a data silo or warehouse which can accommodate any data from any source in one pre-designed format. In this paper we discuss an ETL process that allows users to remodel and store data, and a proposed schema for a common data model which will house this transformed data under one roof. We also perform our own analysis on the proposed data model for a particular use-case;state-wise statistics of data related to pregnancy in India. Thus we prove that the common data model is able to integrate data from different sources and facilitate cross domain analysis and linking of data.

关键词： common data model ETL Extract Transform Load data Warehouse Columnar database Mapping file

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common data model for decision support system of adverse drug reaction to extract knowledge from multi-center database

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INFORMATION TECHNOLOGY & MANAGEMENT 2016年第1期17卷 57-66页

作者： Rho, Mi Jung Kim, Si Ra Park, Seung Ho Jang, Kwang Soo Park, Bum Joon Hong, Joo Young Choi, In Young Catholic Univ Korea Coll Med Dept Med Informat Seoul South Korea Catholic Univ Korea Catholic Inst Healthcare Management Seoul South Korea Catholic Univ Korea Grad Sch Healthcare Management & Policy Seoul South Korea Hanyang Univ Korea Grad Sch Informat Syst Seoul South Korea

An adverse drug reaction (ADR) surveillance system integrated with various electronic medical record (EMR) systems has been suggested as an effective way to collect more data and analyze ADRs earlier than the spontaneous reporting of ADRs. Because Korean hospitals have heterogeneous EMR databases, a common data model (CDM) should first be defined to develop the multi-center EMR-based drug surveillance system. We investigated the data models from two prominent drug safety surveillance studies, the Mini-Sentinel program and the Observational Medical Outcomes Partnership, and developed an EMR-based ADR common data model (EADR CDM). The EADR CDM has eight tables, including a demographic table, drug table, visit table, procedure table, diagnosis table, death table, laboratory table and organization table. Each table consists of 5-12 fields. Among a total of 2,931,060 patients from January 2008 to December 2012 in clinical data warehouse of the S hospital, we extracted the data from 135,745 patients who were prescribed below drugs to determine whether the exported data were sufficient to detect ADRs of six drugs. After validation, we found that the transformed data based on EADR CDM is helpful to understand the prescription pattern and explore feasible medication list for adverse drug signal detection. The collection of diverse data using the CDM is an effective method of early decision of ADRs. This study provides guidelines for developing the CDM and plans to develop the drug safety surveillance system based on multi-center EMR.

关键词： Adverse drug reactions common data model EMR Pharmacovigilance Multi-center database

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Investigation of hepatic adverse events due to quetiapine by using the common data model

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PHARMACOEPIDEMIOLOGY AND DRUG SAFETY 2023年第12期32卷 1341-1349页

作者： Ko, SooJeong Chang, Se-Hyun Chung, Yeon Woong Seo, Young-Gyun Kang, Dong-Yoon Kim, Kwangsoo Chang, Dong-Jin Choi, In Young Catholic Univ Korea Coll Med Dept Med Informat Seoul South Korea Catholic Univ Korea Dept Biomed & Hlth Sci Seoul South Korea Korea Natl Inst Hlth Div Healthcare & Artificial Intelligence Osong Hlth Technol Adm Complex 202 Cheongju South Korea Catholic Univ Korea St Vincents Hosp Coll Med Dept Ophthalmol & Visual Sci Seoul South Korea Hallym Univ Dept Family Med Sacred Heart Hosp Anyang South Korea Ulsan Univ Hosp Dept Prevent Med Ulsan South Korea Seoul Natl Univ Hosp Transdisciplinary Dept Med & Adv Technol Seoul South Korea Catholic Univ Yeouido St Marys Hosp Dept Ophthalmol Korea Coll Med Seoul South Korea

Purpose: Quetiapine is a drug used to treat schizophrenia, bipolar disorder, and major depressive disorder. However, it can cause mild or severe hepatic adverse events and rarely fatal liver damage. This study was aimed at investigating hepatic toxicity caused by quetiapine use by analyzing the information captured from hospital electronic health records by using the Observational Medical Outcomes Partnership common data model (CDM). Methods: This was a retrospective observational study involving a nested case-control method. A CDM based on an electronic health record database from five hospitals between January 2009 and May 2020 was used. We analyzed the status of quetiapine use, adverse events, and hepatic impairment. Results: The numbers of patients with non-serious and severe hepatic adverse reactions were 2566 (5.05%) and 835 (1.64%) out of 50 766 patients, respectively. After adjusting for covariates, the odds ratio of hepatic adverse events was 2.35 (95% CI: 2.03-2.72), and the odds ratio of severe hepatic adverse events was 1.76 (95% CI: 1.16-2.66). Conclusion: Our findings suggest that quetiapine should be cautiously used, and hepatic function should be monitored in patients using quetiapine because it can cause mild or severe hepatic adverse events, complications, and in rare cases, fatal liver damage.

关键词： common data model hepatic adverse events liver function OMOP CDM quetiapine

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Implementation of a Cohort Retrieval System for Clinical data Repositories Using the Observational Medical Outcomes Partnership common data model: Proof-of-Concept System Validation

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JMIR MEDICAL INFORMATICS 2020年第10期8卷 e17376页

作者： Liu, Sijia Wang, Yanshan Wen, Andrew Wang, Liwei Hong, Na Shen, Feichen Bedrick, Steven Hersh, William Liu, Hongfang Mayo Clin Dept Hlth Sci Res 200 First St SW Rochester MN 55901 USA Oregon Hlth & Sci Univ Dept Comp Sci & Elect Engn Portland OR USA Oregon Hlth & Sci Univ Dept Med Informat & Clin Epidemiol Portland OR USA

Background: Widespread adoption of electronic health records has enabled the secondary use of electronic health record data for clinical research and health care delivery. Natural language processing techniques have shown promise in their capability to extract the information embedded in unstructured clinical data, and information retrieval techniques provide flexible and scalable solutions that can augment natural language processing systems for retrieving and ranking relevant records. Objective: In this paper, we present the implementation of a cohort retrieval system that can execute textual cohort selection queries on both structured data and unstructured text-Cohort Retrieval Enhanced by Analysis of Text from Electronic Health Records (CREATE). Methods: CREATE is a proof-of-concept system that leverages a combination of structured queries and information retrieval techniques on natural language processing results to improve cohort retrieval performance using the Observational Medical Outcomes Partnership common data model to enhance model portability. The natural language processing component was used to extract common data model concepts from textual queries. We designed a hierarchical index to support the common data model concept search utilizing information retrieval techniques and frameworks. Results: Our case study on 5 cohort identification queries, evaluated using the precision at 5 information retrieval metric at both the patient-level and document-level, demonstrates that CREATE achieves a mean precision at 5 of 0.90, which outperforms systems using only structured data or only unstructured text with mean precision at 5 values of 0.54 and 0.74, respectively. Conclusions: The implementation and evaluation of Mayo Clinic Biobank data demonstrated that CREATE outperforms cohort retrieval systems that only use one of either structured data or unstructured text in complex textual cohort queries.

关键词： cohort retrieval information retrieval common data model electronic health records natural language processing

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