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Using the electronic health record for genomics research.
Safarova MS, Kullo IJ. Safarova MS, et al. Curr Opin Lipidol. 2020 Apr;31(2):85-93. doi: 10.1097/MOL.0000000000000662. Curr Opin Lipidol. 2020. PMID: 32073412 Free PMC article. Review.
PURPOSE OF REVIEW: Although primarily designed for medical documentation and billing purposes, the electronic health record (EHR) has significant potential for translational research. In this article, we provide an overview of the use of the EHR for …
PURPOSE OF REVIEW: Although primarily designed for medical documentation and billing purposes, the electronic health record …
Electronic health record-based genome-wide meta-analysis provides insights on the genetic architecture of non-alcoholic fatty liver disease.
Ghodsian N, Abner E, Emdin CA, Gobeil É, Taba N, Haas ME, Perrot N, Manikpurage HD, Gagnon É, Bourgault J, St-Amand A, Couture C, Mitchell PL, Bossé Y, Mathieu P, Vohl MC, Tchernof A, Thériault S, Khera AV, Esko T, Arsenault BJ. Ghodsian N, et al. Cell Rep Med. 2021 Nov 3;2(11):100437. doi: 10.1016/j.xcrm.2021.100437. eCollection 2021 Nov 16. Cell Rep Med. 2021. PMID: 34841290 Free PMC article.
We performed a genome-wide meta-analysis of 4 cohorts of electronic health record-documented NAFLD in participants of European ancestry (8,434 cases and 770,180 controls). ...
We performed a genome-wide meta-analysis of 4 cohorts of electronic health record-documented NAFLD in participants of E …
Multi-Omics Profiling for Health.
Babu M, Snyder M. Babu M, et al. Mol Cell Proteomics. 2023 Jun;22(6):100561. doi: 10.1016/j.mcpro.2023.100561. Epub 2023 Apr 27. Mol Cell Proteomics. 2023. PMID: 37119971 Free PMC article. Review.
Additionally, a "one-size-fits all" approach to health care does not take into account individual differences in genetics, environment, or lifestyle factors, decreasing the number of people benefiting from interventions. ...We will briefly discuss the potential of multi-om …
Additionally, a "one-size-fits all" approach to health care does not take into account individual differences in genetics, environmen …
A guide to deep learning in healthcare.
Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, Cui C, Corrado G, Thrun S, Dean J. Esteva A, et al. Nat Med. 2019 Jan;25(1):24-29. doi: 10.1038/s41591-018-0316-z. Epub 2019 Jan 7. Nat Med. 2019. PMID: 30617335 Review.
Our discussion of computer vision focuses largely on medical imaging, and we describe the application of natural language processing to domains such as electronic health record data. Similarly, reinforcement learning is discussed in the context of robotic-ass …
Our discussion of computer vision focuses largely on medical imaging, and we describe the application of natural language processing to doma …
Artificial intelligence for multimodal data integration in oncology.
Lipkova J, Chen RJ, Chen B, Lu MY, Barbieri M, Shao D, Vaidya AJ, Chen C, Zhuang L, Williamson DFK, Shaban M, Chen TY, Mahmood F. Lipkova J, et al. Cancer Cell. 2022 Oct 10;40(10):1095-1110. doi: 10.1016/j.ccell.2022.09.012. Cancer Cell. 2022. PMID: 36220072 Free PMC article. Review.
In oncology, the patient state is characterized by a whole spectrum of modalities, ranging from radiology, histology, and genomics to electronic health records. Current artificial intelligence (AI) models operate mainly in the realm of a single modalit …
In oncology, the patient state is characterized by a whole spectrum of modalities, ranging from radiology, histology, and genomics to …
Using Phecodes for Research with the Electronic Health Record: From PheWAS to PheRS.
Bastarache L. Bastarache L. Annu Rev Biomed Data Sci. 2021 Jul 20;4:1-19. doi: 10.1146/annurev-biodatasci-122320-112352. Annu Rev Biomed Data Sci. 2021. PMID: 34465180 Free PMC article. Review.
Electronic health records (EHRs) are a rich source of data for researchers, but extracting meaningful information out of this highly complex data source is challenging. Phecodes represent one strategy for defining phenotypes for research using EHR data …
Electronic health records (EHRs) are a rich source of data for researchers, but extracting meaningful information out o …
The UK Biobank resource with deep phenotyping and genomic data.
Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, Motyer A, Vukcevic D, Delaneau O, O'Connell J, Cortes A, Welsh S, Young A, Effingham M, McVean G, Leslie S, Allen N, Donnelly P, Marchini J. Bycroft C, et al. Nature. 2018 Oct;562(7726):203-209. doi: 10.1038/s41586-018-0579-z. Epub 2018 Oct 10. Nature. 2018. PMID: 30305743 Free PMC article.
The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follo …
The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each p …
Foundation models for generalist medical artificial intelligence.
Moor M, Banerjee O, Abad ZSH, Krumholz HM, Leskovec J, Topol EJ, Rajpurkar P. Moor M, et al. Nature. 2023 Apr;616(7956):259-265. doi: 10.1038/s41586-023-05881-4. Epub 2023 Apr 12. Nature. 2023. PMID: 37045921 Review.
Built through self-supervision on large, diverse datasets, GMAI will flexibly interpret different combinations of medical modalities, including data from imaging, electronic health records, laboratory results, genomics, graphs or medical text. Models w …
Built through self-supervision on large, diverse datasets, GMAI will flexibly interpret different combinations of medical modalities, includ …
Estimated Prevalence and Clinical Manifestations of UBA1 Variants Associated With VEXAS Syndrome in a Clinical Population.
Beck DB, Bodian DL, Shah V, Mirshahi UL, Kim J, Ding Y, Magaziner SJ, Strande NT, Cantor A, Haley JS, Cook A, Hill W, Schwartz AL, Grayson PC, Ferrada MA, Kastner DL, Carey DJ, Stewart DR. Beck DB, et al. JAMA. 2023 Jan 24;329(4):318-324. doi: 10.1001/jama.2022.24836. JAMA. 2023. PMID: 36692560 Free PMC article.
DESIGN, SETTING, AND PARTICIPANTS: This retrospective observational study evaluated UBA1 variants in exome data from 163 096 participants within the Geisinger MyCode Community Health Initiative. Clinical phenotypes were determined from Geisinger electronic health …
DESIGN, SETTING, AND PARTICIPANTS: This retrospective observational study evaluated UBA1 variants in exome data from 163 096 participants wi …
Deep learning for healthcare: review, opportunities and challenges.
Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Miotto R, et al. Brief Bioinform. 2018 Nov 27;19(6):1236-1246. doi: 10.1093/bib/bbx044. Brief Bioinform. 2018. PMID: 28481991 Free PMC article. Review.
Gaining knowledge and actionable insights from complex, high-dimensional and heterogeneous biomedical data remains a key challenge in transforming health care. Various types of data have been emerging in modern biomedical research, including electronic hea …
Gaining knowledge and actionable insights from complex, high-dimensional and heterogeneous biomedical data remains a key challenge in transf …
1,540 results