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A common data model for the standardization of intensive care unit medication features

JAMIA OPEN 2024年第2期7卷 ooae033页

作者： Sikora, Andrea Keats, Kelli Murphy, David J. Devlin, John W. Smith, Susan E. Murray, Brian Buckley, Mitchell S. Rowe, Sandra Coppiano, Lindsey Kamaleswaran, Rishikesan Univ Georgia Dept Clin & Adm Pharm Coll Pharm 1120 15th StHM 118 Augusta GA 30912 USA Augusta Univ Dept Pharm Med Ctr Augusta GA 30912 USA Emory Univ Div Pulm Allergy Crit Care & Sleep Med Atlanta GA 30322 USA Northeastern Univ Sch Pharm Boston MA 02115 USA Brigham & Womens Hosp Div Pulm & Crit Care Med 75 Francis St Boston MA 02115 USA Univ Georgia Coll Pharm Dept Clin & Adm Pharm Athens GA 30601 USA Univ North Carolina Med Ctr Dept Pharm Chapel Hill NC 27514 USA Banner Univ Med Ctr Phoenix Dept Pharm Phoenix AZ 85032 USA Oregon Hlth & Sci Univ Dept Pharm Portland OR 97239 USA Emory Univ Sch Med Dept Biomed Informat Atlanta GA 30322 USA Georgia Inst Technol Dept Biomed Engn Atlanta GA 30322 USA

Objective: common data models provide a standard means of describing data for artificial intelligence (AI) applications, but this process has never been undertaken for medications used in the intensive care unit (ICU). We sought to develop a common data model (CDM) for ICU medications to standardize the medication features needed to support future ICU AI efforts. Materials and Methods: A 9-member, multi-professional team of ICU clinicians and AI experts conducted a 5-round modified Delphi process employing conference calls, web-based communication, and electronic surveys to define the most important medication features for AI efforts. Candidate ICU medication features were generated through group discussion and then independently scored by each team member based on relevance to ICU clinical decision-making and feasibility for collection and coding. A key consideration was to ensure the final ontology both distinguished unique medications and met Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles. Results: Using a list of 889 ICU medications, the team initially generated 106 different medication features, and 71 were ranked as being core features for the CDM. Through this process, 106 medication features were assigned to 2 key feature domains: drug product-related (n=43) and clinical practice-related (n=63). Each feature included a standardized definition and suggested response values housed in the electronic data library. This CDM for ICU medications is available online. Conclusion: The CDM for ICU medications represents an important first step for the research community focused on exploring how AI can improve patient outcomes and will require ongoing engagement and refinement. Lay Summary Medication data pose a unique challenge for interpretation by artificial intelligence (AI) because of its alphanumerical combinations (eg, ibuprofen 200 mg every 4 hours) and the technical detail associated with drug prescriptions (eg, ibuprofen 200 mg and

关键词： artificial intelligence common data model critical care

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Coronary Artery Computed Tomography Angiography for Preventing Cardio-Cerebrovascular Disease: Observational Cohort Study Using the Observational Health data Sciences and Informatics' common data model

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JMIR MEDICAL INFORMATICS 2022年第10期10卷 e41503页

作者： Bae, Woo Kyung Cho, Jihoon Kim, Seok Kim, Borham Baek, Hyunyoung Song, Wongeun Yoo, Sooyoung Seoul Natl Univ Dept Family Med Hlth Promot Ctr Bundang Hosp Seongnam Si Gyeonggi Do South Korea Seoul Natl Univ Healthcare Informat & Commun Technol Res Ctr Off eHlth Res & Business Bundang Hosp Seongnam Si South Korea

Background: Cardio-cerebrovascular diseases (CVDs) result in 17.5 million deaths annually worldwide, accounting for 46.2% of noncommunicable causes of death, and are the leading cause of death, followed by cancer, respiratory disease, and diabetes mellitus. Coronary artery computed tomography angiography (CCTA), which detects calcification in the coronary arteries, can be used to detect asymptomatic but serious vascular disease. It allows for noninvasive and quick testing despite involving radiation exposure. Objective: The objective of our study was to investigate the effectiveness of CCTA screening on CVD outcomes by using the Observational Health data Sciences and Informatics' Observational Medical Outcomes Partnership common data model (OMOP-CDM) data and the population-level estimation method. Methods: Using electronic health record-based OMOP-CDM data, including health questionnaire responses, adults (aged 30-74 years) without a history of CVD were selected, and 5-year CVD outcomes were compared between patients undergoing CCTA (target group) and a comparison group via 1:1 propensity score matching. Participants were stratified into low-risk and high-risk groups based on the American College of Cardiology/American Heart Association atherosclerotic cardiovascular disease (ASCVD) risk score and Framingham risk score (FRS) for subgroup analyses. Results: The 2-year and 5-year risk scores were compared as secondary outcomes between the two groups. In total, 8787 participants were included in both the target group and comparison group. No significant differences (calibration P=.37) were found between the hazard ratios of the groups at 5 years. The subgroup analysis also revealed no significant differences between the ASCVD risk scores and FRSs of the groups at 5 years (ASCVD risk score: P=.97;FRS: P=.85). However, the CCTA group showed a significantly lower increase in risk scores at 2 years (ASCVD risk score: P=.03;FRS: P=.02). Conclusions: Although we could not c

关键词： cardiovascular diseases coronary artery computed tomography angiography observational study common data model population level estimation cardiology vascular disease medical informatics computed tomography angiography electronic health record risk score health data science data modeling

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Patient-Level Fall Risk Prediction Using the Observational Medical Outcomes Partnership's common data model: Pilot Feasibility Study

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JMIR MEDICAL INFORMATICS 2022年第3期10卷 e35104页

作者： Jung, Hyesil Yoo, Sooyoung Kim, Seok Heo, Eunjeong Kim, Borham Lee, Ho-Young Hwang, Hee Seoul Natl Univ Bundang Hosp Off eHlth Res & Business 172 Dolma Ro Seongnam Si 13620 South Korea Kakao Healthcare Co Co Seongnam Si South Korea

Background: Falls in acute care settings threaten patients' safety. Researchers have been developing fall risk prediction models and exploring risk factors to provide evidence-based fall prevention practices;however, such efforts are hindered by insufficient samples, limited covariates, and a lack of standardized methodologies that aid study replication. Objective: The objectives of this study were to (1) convert fall-related electronic health record data into the standardized Observational Medical Outcome Partnership's (OMOP) common data model format and (2) develop models that predict fall risk during 2 time periods. Methods: As a pilot feasibility test, we converted fall-related electronic health record data (nursing notes, fall risk assessment sheet, patient acuity assessment sheet, and clinical observation sheet) into standardized OMOP common data model format using an extraction, transformation, and load process. We developed fall risk prediction models for 2 time periods (within 7 days of admission and during the entire hospital stay) using 2 algorithms (least absolute shrinkage and selection operator logistic regression and random forest). Results: In total, 6277 nursing statements, 747,049,486 clinical observation sheet records, 1,554,775 fall risk scores, and 5,685,011 patient acuity scores were converted into OMOP common data model format. All our models (area under the receiver operating characteristic curve 0.692-0.726) performed better than the Hendrich II Fall Risk model. Patient acuity score, fall history, age >= 60 years, movement disorder, and central nervous system agents were the most important predictors in the logistic regression models. Conclusions: To enhance model performance further, we are currently converting all nursing records into the OMOP common data model data format, which will then be included in the models. Thus, in the near future, the performance of fall risk prediction models could be improved through the application of abundan

关键词： common data model accidental falls Observational Medical Outcomes Partnership nursing records medical informatics health data electronic health record data model prediction model risk prediction fall risk

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Building the observational medical outcomes partnership's T-MSIS Analytic File common data model

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Informatics in Medicine Unlocked 2023年 39卷 101259页

作者： Williams, Nick National Library of Medicine Lister Hill National Center for Biomedical Communications Bethesda MD United States

Objectives: This effort used databricks to create an Observational Medical Outcomes Partnership (OMOP) common data model (CDM) for Transformed MSIS Analytic File (TAF) Medicaid records. Materials and methods: Our process included data volume and content assessment of TAF, translation mapping of TAF concepts to OMOP concepts and the creation of Extract Transform and Load (ETL) code. Results: The final CDM contained 119, 048, 562 individuals and 24, 806, 828,121 clinical observations from 2014 through 2018. Discussion: The transformation of TAF into OMOP can support the generation of evidence with special attention to low-income patients on public insurance. Such patients are perhaps underrepresented in academic medical center patient populations. Conclusion: Our effort successfully used databricks to transform TAF records into OMOP CDM. Our CDM can be used to generate evidence for OMOP network studies. © 2023

关键词： Centers for medicare and medicaid services common data model Medicaid Observational health data sciences and informatics Observational medical outcomes partnership

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Conversion of CPRD AURUM data into the OMOP common data model

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Informatics in Medicine Unlocked 2023年 43卷 101407页

作者： Mayer, Craig S. Lister Hill National Center for Biomedical Communication National Library of Medicine 8600 Rockville Pike Bethesda NIH Bethesda 20894 MD United States

Introduction: Efforts to standardize clinical data using common data models (CDMS) has grown in recent years. Use of CDMs allows for quicker understanding of data structure and reuse of existing tools. One CDM is the Observational Medical Outcomes Partnership (OMOP) CDM. Clinical Practice Research datalink (CPRD) is a data collection program collecting general practitioner data in the UK. Objective: Our objective was to convert a static copy of CPRD AURUM data into the OMOP CDM and run existing tools on the converted data. Methods: Two methods were used to convert each CPRD file into the OMOP CDM. The first was direct mapping used when converting CPRD files that had comparable tables in the OMOP CDM. The original names were changed to the OMOP equivalent and source values converted to standardized OMOP concepts. CPRD files: Patient (to OMOP Person), Staff (to Provider), Drug Issue (to Drug Exposure) and Practice (to Care Site) were directly mapped. The second method was indirect where for the CPRD Observation file the domain of each data row was used to assign data to proper OMOP tables or columns done by converting all source values to standard concepts. Results: The OMOP CDM conversion populated 12 tables and 20,240,453,339 rows, with the largest table being the Measurement table (5,202,579,174 data row). Mapping source values to OMOP standard concepts, we found 60.2% (46,413 of 77,149) of source concepts were also standard concepts. The Drug Exposure table had the fewest source values already in the standard form as only 4.7% (1433 of 30,194) of the source concepts were standard concepts. On a data retention level, only 2.00% of all data rows were excluded as they did not have a clear fit in the developed CDM and were not able to stand alone without additional information which was not present. Conclusion: CPRD AURUM was successfully converted into the OMOP CDM with minimal data loss. Existing OHDSI tools were used with the converted data to show efficacy of the

关键词： Clinical informatics common data model data science Real world data

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Long-term use of proton-pump inhibitor on Alzheimer's disease: a real-world distributed network analysis of six observational Korean databases using a common data model

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THERAPEUTIC ADVANCES IN NEUROLOGICAL DISORDERS 2022年 15卷 17562864221135700页

作者： Kim, Yerim Seo, Seung In Lee, Kyung Joo Kim, Jinseob Yoo, Jong Jin Seo, Won-Woo Lee, Hyung Seok Shin, Woon Geon Hallym Univ Kangdong Sacred Heart Hosp Coll Med Dept Internal MedDiv Gastroenterol 150 Seongan Ro Seoul 05355 South Korea Hallym Univ Inst Liver & Digest Dis Chunchon South Korea Hallym Univ Kangdong Sacred Heart Hosp Coll Med Dept Neurol Seoul South Korea Hallym Univ Kangdong Sacred Heart Hosp Coll Med Dept Internal MedDiv Gastroenterol Seoul South Korea Hallym Univ Univ Ind Fdn Chunchon South Korea Seoul Natl Univ Sch Publ Hlth Dept Epidemiol Seoul South Korea Hallym Univ Kangdong Sacred Heart Hosp Coll Med Dept Internal MedDiv Rheumatol Seoul South Korea Hallym Univ Kangdong Sacred Heart Hosp Coll Med Dept Internal MedDiv Cardiol Seoul South Korea Hallym Univ Sacred Heart Hosp Coll Med Dept Internal MedDiv Nephrol Anyang South Korea

Background:Dementia has a crucial impact on the quality of life of elderly patients and their caregivers. Proton-pump inhibitors (PPIs) are the most frequently prescribed treatment, but they have been shown to be associated with dementia. The data are inconsistent, however. Objective:To investigate the association between PPIs use and Alzheimer's disease (AD) or all-cause dementia in six observational Korean databases using a common data model (CDM) and to perform a distributed network analysis. Methods:Subjects aged over 18 years between 1 January 2004 and 31 December 2020. Among 7,293,565 subjects from 6 cohorts, 41,670 patients met the eligibility criteria. A total of 2206 patients who were included in both cohorts or with a history of dementia were excluded. After propensity matching, 5699 propensity-matched pairs between the PPIs and histamine-2 receptor antagonist (H(2)RA) users were included in this study. The primary outcome was the incidence of AD at least 365 days after drug exposure. The secondary outcome was the incidence of all-cause dementia at least 365 days after drug exposure. Results:In the 1:1 propensity score matching, the risk of AD or all-cause dementia was not significantly different between the PPIs and H(2)RA groups in all six databases. In the distributed network analysis, the long-term PPI users (> 365 days) were unassociated with AD [hazard ratio (HR) = 0.92, 95% confidence interval (CI) = 0.68-1.23;I-2 = 0%] and all-cause dementia (HR =1.04, 95% CI = 0.82-1.31;I-2 = 0%) compared with H(2)RA users. Conclusion:In the distributed network analysis of six Korean hospital databases using Observational Medical Outcomes Partnership (OMOP)-CDM data, the long-term use of PPI was not associated with a statistically significantly increased risk of AD or all-cause dementia. Therefore, we suggest that physicians should not avoid these medications because of concern about dementia risk.

关键词： Alzheimer disease common data model dementia H-2 receptor antagonist proton-pump inhibitors

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data Quality- and Utility-Compliant Anonymization of common data model-Harmonized Electronic Health Record data: Protocol for a Scoping Review

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JMIR RESEARCH PROTOCOLS 2023年 12卷 e46471页

作者： Wabo, Gaetan Kamdje Prasser, Fabian Gierend, Kerstin Siegel, Fabian Ganslandt, Thomas Heidelberg Univ Mannheim Med Fac Ctr Prevent Med & Digital Hlth Baden Wurttemberg Dept Biomed Informat Theodor Kutzer Ufer 1-3House 3Floor 4 D-68167 Mannheim Germany Charite Univ Med Berlin Berlin Inst Hlth Berlin Germany Heidelberg Univ Univ Med Ctr Mannheim Mannheim Med Fac Dept Urol & Urosurgery Mannheim Germany Friedrich Alexander Univ Erlangen Nurnberg Chair Med Informat Erlangen Germany

Background: The anonymization of common data model (CDM)-converted EHR data is essential to ensure the data privacy in the use of harmonized health care data. However, applying data anonymization techniques can significantly affect many properties of the resulting data sets and thus biases research results. Few studies have reviewed these applications with a reflection of approaches to manage data utility and quality concerns in the context of CDM-formatted health care ***: Our intended scoping review aims to identify and describe (1) how formal anonymization methods are carried out with CDM-converted health care data, (2) how data quality and utility concerns are considered, and (3) how the various CDMs differ in terms of their suitability for recording anonymized data. Methods: The planned scoping review is based on the framework of Arksey and O'Malley. By using this, only articles published in English will be included. The retrieval of literature items should be based on a literature search string combining keywords related to data anonymization, CDM standards, and data quality assessment. The proposed literature search query should be validated by a librarian, accompanied by manual searches to include further informal sources. Eligible articles will first undergo a deduplication step, followed by the screening of titles. Second, a full-text reading will allow the 2 reviewers involved to reach the final decision about article selection, while a domain expert will support the resolution of citation selection conflicts. Additionally, key information will be extracted, categorized, summarized, and analyzed by using a proposed template into an iterative process. Tabular and graphical analyses should be addressed in alignment with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. We also performed some tentative searches on Web of Science for estimating the feasibility of reaching el

关键词： EHR electronic health record data quality common data model data standard data privacy models data anonymization

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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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Analysis of antiseizure drug-related adverse reactions from the electronic health record using the common data model

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EPILEPSIA 2020年第4期61卷 610-616页

作者： Choi, Sun Ah Kim, Hunmin Kim, Seok Yoo, Sooyoung Yi, Soyoung Jeon, Yonghoon Hwang, Hee Kim, Ki Joong Seoul Natl Univ Coll Med Dept Pediat Bundang Hosp Seongnam South Korea Dankook Univ Dankook Univ Hosp Dept Pediat Coll Med Cheonan South Korea Seoul Natl Univ Bundang Hosp Off eHlth Res & Business Seongnam South Korea Seoul Natl Univ Coll Med Dept Pediat Childrens Hosp Seoul South Korea

Objective Antiseizure drugs (ASDs) are known to cause a wide range of adverse drug reactions (ADRs). Recently, electronic health care data using the common data model (CDM) have been introduced and commonly adopted in pharmacovigilance research. We aimed to analyze ASD-related ADRs using CDM and to assess the feasibility of CDM analysis in monitoring ADR in a single tertiary hospital. Methods We selected five ASDs: oxcarbazepine (OXC), lamotrigine (LTG), levetiracetam (LEV), valproic acid (VPA), and topiramate (TPM). Patients diagnosed with epilepsy and exposed to monotherapy with one of the ASDs before age 18 years were included. We measured four ADR outcomes: (1) hematologic abnormality, (2) hyponatremia, (3) elevation of liver enzymes, and (4) subclinical hypothyroidism. We performed a subgroup analysis to exclude the effects of concomitant medications. Results From the database, 1344 patients were included for the study. Of the 1344 patients, 436 were receiving OXC, 293 were receiving LTG, 275 were receiving LEV, 180 were receiving VPA, and 160 were receiving TPM. Thrombocytopenia developed in 14.1% of patients taking VPA. Hyponatremia occurred in 10.5% of patients taking OXC. Variable ranges of liver enzyme elevation were detected in 19.3% of patients taking VPA. Subclinical hypothyroidism occurred in approximately 21.5% to 28% of patients with ASD monotherapy, which did not significantly differ according to the type of ASD. In a subgroup analysis, we observed similar ADR tendencies, but with less thrombocytopenia in the TPM group. Significance The incidence and trends of ADRs that were evaluated by CDM were similar to the previous literature. CDM can be a useful tool for analyzing ASD-related ADRs in a multicenter study. The strengths and limitations of CDM should be carefully addressed.

关键词： adverse drug reaction antiseizure drugs common data model epilepsy pharmacovigilance

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Transformation of Pathology Reports Into the common data model With Oncology Module: Use Case for Colon Cancer

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JOURNAL OF MEDICAL INTERNET RESEARCH 2020年第12期22卷 e18526页

作者： Ryu, Borim Yoon, Eunsil Kim, Seok Lee, Sejoon Baek, Hyunyoung Yi, Soyoung Na, Hee Young Kim, Ji-Won Baek, Rong-Min Hwang, Hee Yoo, Sooyoung Seoul Natl Univ Bundang Hosp Off eHlth Res & Business 82 173rd St Seongnam 13620 South Korea Seoul Natl Univ Dept Pathol & Translat Med Bundang Hosp Seongnam South Korea Seoul Natl Univ Div Hematol & Med Oncol Bundang Hosp Seongnam South Korea Seoul Natl Univ Dept Plast Surg Bundang Hosp Seongnam South Korea

Background: common data models (CDMs) help standardize electronic health record data and facilitate outcome analysis for observational and longitudinal research. An analysis of pathology reports is required to establish fundamental information infrastructure for data-driven colon cancer research. The Observational Medical Outcomes Partnership (OMOP) CDM is used in distributed research networks for clinical data;however, it requires conversion of free text-based pathology reports into the CDM's format. There are few use cases of representing cancer data in CDM. Objective: In this study, we aimed to construct a CDM database of colon cancer-related pathology with natural language processing (NLP) for a research platform that can utilize both clinical and omics data. The essential text entities from the pathology reports are extracted, standardized, and converted to the OMOP CDM format in order to utilize the pathology data in cancer research. Methods: We extracted clinical text entities, mapped them to the standard concepts in the Observational Health data Sciences and Informatics vocabularies, and built databases and defined relations for the CDM tables. Major clinical entities were extracted through NLP on pathology reports of surgical specimens, immunohistochemical studies, and molecular studies of colon cancer patients at a tertiary general hospital in South Korea. Items were extracted from each report using regular expressions in Python. Unstructured data, such as text that does not have a pattern, were handled with expert advice by adding regular expression rules. Our own dictionary was used for normalization and standardization to deal with biomarker and gene names and other ungrammatical expressions. The extracted clinical and genetic information was mapped to the Logical Observation Identifiers Names and Codes databases and the Systematized Nomenclature of Medicine (SNOMED) standard terminologies recommended by the OMOP CDM. The database-table relationships we

关键词： common data model natural language processing oncology module colon cancer electronic health record oncology pathology clinical data

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