MIMIC-III, a freely accessible critical care database
Alistair E. W. Johnson,Tom J. Pollard,Lu Shen,Li-wei H. Lehman,Mengling Feng,Mengling Feng,Mohammad M. Ghassemi,Benjamin Moody,Peter Szolovits,Leo Anthony Celi,Leo Anthony Celi,Roger G. Mark,Roger G. Mark +12 more
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TLDR
The Medical Information Mart for Intensive Care (MIMIC-III) as discussed by the authors is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital.Abstract:
MIMIC-III ('Medical Information Mart for Intensive Care') is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. The database supports applications including academic and industrial research, quality improvement initiatives, and higher education coursework.read more
Citations
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Journal ArticleDOI
A guide to deep learning in healthcare.
Andre Esteva,Alexandre Robicquet,Bharath Ramsundar,Volodymyr Kuleshov,Mark A. DePristo,Katherine Chou,Claire Cui,Greg S. Corrado,Sebastian Thrun,Jeffrey Dean +9 more
TL;DR: How these computational techniques can impact a few key areas of medicine and explore how to build end-to-end systems are described.
Journal ArticleDOI
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
Recurrent Neural Networks for Multivariate Time Series with Missing Values.
TL;DR: In this article, a deep learning model based on Gated Recurrent Unit (GRU) is proposed to exploit the missing values and their missing patterns for effective imputation and improving prediction performance.
Book ChapterDOI
C-store: a column-oriented DBMS
Michael Stonebraker,Daniel J. Abadi,Adam Batkin,Xuedong Chen,Mitch Cherniack,Miguel Ferreira,Edmond Lau,Amerson Lin,Samuel Madden,Elizabeth O'Neil,Patrick O'Neil,Alexander Rasin,Nga Tran,Stan Zdonik +13 more
TL;DR: Preliminary performance data on a subset of TPC-H is presented and it is shown that the system the team is building, C-Store, is substantially faster than popular commercial products.
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.
References
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PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.
Ary L. Goldberger,Luís A. Nunes Amaral,Leon Glass,Jeffrey M. Hausdorff,Plamen Ch. Ivanov,Roger G. Mark,Joseph E. Mietus,George B. Moody,Chung-Kang Peng,H. Eugene Stanley +9 more
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Mohammed Saeed,Mauricio Villarroel,Andrew T. Reisner,Gari D. Clifford,Gari D. Clifford,Li-wei H. Lehman,George B. Moody,Thomas Heldt,Tin H. Kyaw,Benjamin Moody,Roger G. Mark,Roger G. Mark +11 more
TL;DR: MIMIC-II documents a diverse and very large population of intensive care unit patient stays and contains comprehensive and detailed clinical data, including physiological waveforms and minute-by-minute trends for a subset of records.
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Best Practices for Scientific Computing
Greg Wilson,D. A. Aruliah,C. Titus Brown,Neil Chue Hong,Matt Davis,Richard T. Guy,Steven H. D. Haddock,Kathryn D. Huff,Ian M. Mitchell,Mark D. Plumbley,Ben Waugh,Ethan P. White,Paul P. H. Wilson +12 more
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Ishna Neamatullah,Margaret M Douglass,Li-wei H. Lehman,Andrew T. Reisner,Mauricio Villarroel,William J. Long,Peter Szolovits,George B. Moody,Roger G. Mark,Gari D. Clifford +9 more
TL;DR: In this article, an automated Perl-based de-identification software package is described that is generally usable on most free-text medical records, e.g., nursing notes, discharge summaries, X-ray reports, etc.
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