Validation of Electronic Medical Record-Based Phenotyping Algorithms: Results and Lessons Learned From the eMERGE Network
Katherine M. Newton,Peggy L. Peissig,Abel N. Kho,Suzette J. Bielinski,Richard L. Berg,Vidhu Choudhary,Melissa A. Basford,Christopher G. Chute,Iftikhar J. Kullo,Rongling Li,Jennifer A. Pacheco,Luke V. Rasmussen,Leslie Spangler,Joshua C. Denny +13 more
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TLDR
Validation is a worthwhile process that not only measures phenotype performance but also strengthens phenotype algorithm definitions and enhances their inter-institutional sharing.About:
This article is published in Journal of the American Medical Informatics Association.The article was published on 2013-06-01 and is currently open access. It has received 349 citations till now. The article focuses on the topics: Validation Studies as Topic.read more
Citations
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Scalable and accurate deep learning with electronic health records
Alvin Rajkomar,Alvin Rajkomar,Eyal Oren,Kai Chen,Andrew M. Dai,Nissan Hajaj,Michaela Hardt,Peter J. Liu,Xiaobing Liu,Jake Marcus,Mimi Sun,Patrik Sundberg,Hector Yee,Kun Zhang,Yi Zhang,Gerardo Flores,Gavin E. Duggan,Jamie Irvine,Quoc V. Le,Kurt Litsch,Alexander Mossin,Justin Tansuwan,De Wang,James Wexler,Jimbo Wilson,Dana Ludwig,Samuel L. Volchenboum,Katherine Chou,Michael Pearson,Srinivasan Madabushi,Nigam H. Shah,Atul J. Butte,Michael D. Howell,Claire Cui,Greg S. Corrado,Jeffrey Dean +35 more
TL;DR: A representation of patients’ entire raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format is proposed, and it is demonstrated that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization.
Journal ArticleDOI
Scalable and accurate deep learning for electronic health records
Alvin Rajkomar,Eyal Oren,Kai Chen,Andrew M. Dai,Nissan Hajaj,Peter J. Liu,Xiaobing Liu,Mimi Sun,Patrik Sundberg,Hector Yee,Kun Zhang,Gavin E. Duggan,Gerardo Flores,Michaela Hardt,Jamie Irvine,Quoc V. Le,Kurt Litsch,Jake Marcus,Alexander Mossin,Justin Tansuwan,De Wang,James Wexler,Jimbo Wilson,Dana Ludwig,Samuel L. Volchenboum,Katherine Chou,Michael Pearson,Srinivasan Madabushi,Nigam H. Shah,Atul J. Butte,Michael D. Howell,Claire Cui,Greg S. Corrado,Jeffrey Dean +33 more
TL;DR: In this paper, the authors proposed a representation of patients' entire, raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format and demonstrated that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization.
Journal ArticleDOI
Identification of type 2 diabetes subgroups through topological analysis of patient similarity
Li Li,Wei-Yi Cheng,Benjamin S. Glicksberg,Omri Gottesman,Ronald Tamler,Rong Chen,Erwin P. Bottinger,Joel T. Dudley +7 more
TL;DR: The authors found that classical T2D features such as obesity, high blood sugar, kidney disease, and eye disease, were limited to subtype 1, whereas other comorbidities such as cancer and neurological diseases were specific to subtypes 2 and 3, respectively.
Journal ArticleDOI
Preparing Medical Imaging Data for Machine Learning.
Martin J. Willemink,Wojciech A. Koszek,Cailin Hardell,Jie Wu,Dominik Fleischmann,Hugh Harvey,Les R. Folio,Ronald M. Summers,Daniel L. Rubin,Matthew P. Lungren +9 more
TL;DR: Fundamental steps for preparing medical imaging data in AI algorithm development are described, current limitations to data curation are explained, and new approaches to address the problem of data availability are explored.
Journal ArticleDOI
PheKB: A catalog and workflow for creating electronic phenotype algorithms for transportability
Jacqueline Kirby,Peter Speltz,Luke V. Rasmussen,Melissa A. Basford,Omri Gottesman,Peggy L. Peissig,Jennifer A. Pacheco,Gerard Tromp,Jyotishman Pathak,David Carrell,Stephen B. Ellis,Todd Lingren,William K. Thompson,Guergana Savova,Jonathan L. Haines,Dan M. Roden,Paul A. Harris,Joshua C. Denny +17 more
TL;DR: These results demonstrate that a broad range of algorithms to mine electronic health record data from different health systems can be developed with high PPV, and algorithms developed at one site are generally transportable to others.
References
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TL;DR: In this paper, the value of electronic health care information exchange and interoperability (HIEI) between providers (hospitals and medical group practices) and independent laboratories, radiology centers, pharmacies, payers, public health departments, and other providers.
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Jan Walker,Eric C. Pan,Douglas R. Johnston,Julia Adler-Milstein,David W. Bates,Blackford Middleton +5 more
TL;DR: In this paper, the value of electronic health care information exchange and interoperability (HIEI) between providers and independent laboratories, radiology centers, pharmacies, payers, public health departments, and other providers is assessed.
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The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies.
Catherine A. McCarty,Rex L. Chisholm,Christopher G. Chute,Iftikhar J. Kullo,Gail P. Jarvik,Eric B. Larson,Rongling Li,Daniel R. Masys,Marylyn D. Ritchie,Dan M. Roden,Jeffery P. Struewing,Wendy A. Wolf +11 more
TL;DR: By combining advanced clinical informatics, genome science, and community consultation, eMERGE represents a first step in the development of data-driven approaches to incorporate genomic information into routine healthcare delivery.
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The PhenX Toolkit: Get the Most From Your Measures
Carol Hamilton,Lisa Strader,Joseph Pratt,Deborah Maiese,Tabitha Hendershot,Richard K. Kwok,Jane Hammond,Wayne Huggins,Dean Jackman,Huaqin Pan,Destiney Nettles,Destiney Nettles,Terri H. Beaty,Lindsay A. Farrer,Peter Kraft,Mary L. Marazita,Jose M. Ordovas,Carlos N. Pato,Margaret R. Spitz,Diane Wagener,Michelle A. Williams,Heather Junkins,William R. Harlan,Erin M. Ramos,Jonathan L. Haines +24 more
TL;DR: The PhenX Toolkit provides the research community with a core set of high-quality, well-established, low-burden measures intended for use in large-scale genomic studies and includes links to standards and resources in an effort to facilitate data harmonization to legacy data.
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Electronic Medical Records for Genetic Research: Results of the eMERGE Consortium
Abel N. Kho,Jennifer A. Pacheco,Peggy L. Peissig,Luke V. Rasmussen,Katherine M. Newton,Katherine M. Newton,Noah Weston,Paul K. Crane,Jyotishman Pathak,Christopher G. Chute,Suzette J. Bielinski,Iftikhar J. Kullo,Rongling Li,Teri A. Manolio,Rex L. Chisholm,Joshua C. Denny +15 more
TL;DR: It is concluded that widespread adoption of electronic medical records will provide real-world clinical data that will be valuable for genome-wide association studies (GWAS) and other types of genetic research.
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