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Research and Reporting Considerations for Observational Studies Using Electronic Health Record Data.
Ann Intern Med. 2020 Jun 2;172(11 Suppl):S79-S84. doi: 10.7326/M19-0873.
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Feasibility analysis of conducting observational studies with the electronic health record.
BMC Med Inform Decis Mak. 2019 Oct 28;19(1):202. doi: 10.1186/s12911-019-0939-0.
3
Electronic Health Record Challenges, Workarounds, and Solutions Observed in Practices Integrating Behavioral Health and Primary Care.
J Am Board Fam Med. 2015 Sep-Oct;28 Suppl 1(Suppl 1):S63-72. doi: 10.3122/jabfm.2015.S1.150133.
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Clinical research informatics and electronic health record data.
Yearb Med Inform. 2014 Aug 15;9(1):215-23. doi: 10.15265/IY-2014-0009.
5
A method for cohort selection of cardiovascular disease records from an electronic health record system.
Int J Med Inform. 2017 Jun;102:138-149. doi: 10.1016/j.ijmedinf.2017.03.015. Epub 2017 Mar 30.
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From real-world electronic health record data to real-world results using artificial intelligence.
Ann Rheum Dis. 2023 Mar;82(3):306-311. doi: 10.1136/ard-2022-222626. Epub 2022 Sep 23.
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Quality of Reporting Electronic Health Record Data in Glaucoma: A Systematic Literature Review.
Ophthalmol Glaucoma. 2024 Sep-Oct;7(5):422-430. doi: 10.1016/j.ogla.2024.04.002. Epub 2024 Apr 8.
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Promises and pitfalls of electronic health record analysis.
Diabetologia. 2018 Jun;61(6):1241-1248. doi: 10.1007/s00125-017-4518-6. Epub 2017 Dec 15.

引用本文的文献

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A Standardized Guideline for Assessing Extracted Electronic Health Records Cohorts: A Scoping Review.
AMIA Jt Summits Transl Sci Proc. 2025 Jun 10;2025:527-536. eCollection 2025.
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Dynamic Prediction and Intervention of Serum Sodium in Patients with Stroke Based on Attention Mechanism Model.
J Healthc Inform Res. 2025 Mar 6;9(2):174-190. doi: 10.1007/s41666-025-00192-x. eCollection 2025 Jun.
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Development and validation of a machine learning risk prediction model for asthma attacks in adults in primary care.
NPJ Prim Care Respir Med. 2025 Apr 23;35(1):24. doi: 10.1038/s41533-025-00428-8.
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A scoping review of OMOP CDM adoption for cancer research using real world data.
NPJ Digit Med. 2025 Apr 7;8(1):189. doi: 10.1038/s41746-025-01581-7.
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Beyond Phecodes: leveraging PheMAP to identify patients lacking diagnosis codes in electronic health records.
J Am Med Inform Assoc. 2025 Jun 1;32(6):1007-1014. doi: 10.1093/jamia/ocaf055.
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Clinical entity augmented retrieval for clinical information extraction.
NPJ Digit Med. 2025 Jan 19;8(1):45. doi: 10.1038/s41746-024-01377-1.
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Value sets and the problem of redundancy in value set repositories.
PLoS One. 2024 Dec 9;19(12):e0312289. doi: 10.1371/journal.pone.0312289. eCollection 2024.

本文引用的文献

1
Feasibility of Using Real-World Data to Replicate Clinical Trial Evidence.
JAMA Netw Open. 2019 Oct 2;2(10):e1912869. doi: 10.1001/jamanetworkopen.2019.12869.
2
Concordance Between Electronic Clinical Documentation and Physicians' Observed Behavior.
JAMA Netw Open. 2019 Sep 4;2(9):e1911390. doi: 10.1001/jamanetworkopen.2019.11390.
3
Prevalence and Predictability of Low-Yield Inpatient Laboratory Diagnostic Tests.
JAMA Netw Open. 2019 Sep 4;2(9):e1910967. doi: 10.1001/jamanetworkopen.2019.10967.
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Advances in Electronic Phenotyping: From Rule-Based Definitions to Machine Learning Models.
Annu Rev Biomed Data Sci. 2018 Jul;1:53-68. doi: 10.1146/annurev-biodatasci-080917-013315. Epub 2018 May 23.
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A Comparison of Data Quality Assessment Checks in Six Data Sharing Networks.
EGEMS (Wash DC). 2017 Jun 12;5(1):8. doi: 10.5334/egems.223.

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