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Articles Published Processes
11/21/2019 10:02:33 AM | Browse: 850 | Download: 1534
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Received |
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2019-04-23 00:51 |
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Peer-Review Started |
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2019-05-08 03:51 |
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To Make the First Decision |
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2019-08-06 01:13 |
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Return for Revision |
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2019-08-06 06:18 |
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Revised |
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2019-08-21 21:28 |
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Second Decision |
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2019-10-25 06:37 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2019-10-29 00:14 |
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Articles in Press |
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2019-10-29 00:14 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2019-11-08 11:53 |
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Publish the Manuscript Online |
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2019-11-21 10:02 |
| ISSN |
2220-3141(online) |
| Open Access |
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2019. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Critical Care Medicine |
| Manuscript Type |
Retrospective Cohort Study |
| Article Title |
Machine learning in data abstraction: A computable phenotype for sepsis and septic shock diagnosis in the intensive care unit
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Prabij Dhungana, Laura Piccolo Serafim, Arnaldo Lopez Ruiz, Danette Bruns, Timothy J Weister, Nathan Jerome Smischney and Rahul Kashyap |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Rahul Kashyap, MBBS, Assistant Professor, Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States. kashyap.rahul@mayo.edu |
| Key Words |
Machine learning; Computable phenotype; Critical care; Sepsis; Septic shock; |
| Core Tip |
This study presents and validates a supervised machine learning model for the identification of sepsis and septic shock cases using electronic medical records as an alternative to manual chart review. This method showed to be an efficient, fast and reliable option for retrospective data abstraction, with the potential to be applied to other clinical conditions. |
| Publish Date |
2019-11-21 10:02 |
| Citation |
Dhungana P, Piccolo Serafim L, Lopez Ruiz A, Bruns D, Weister TJ, Smichney NJ, Kashyap R. Machine learning in data abstraction: A computable phenotype for sepsis and septic shock diagnosis in the intensive care unit. World J Crit Care Med 2019; 8(7): 120-126 |
| URL |
https://www.wjgnet.com/2220-3141/full/v8/i7/120.htm |
| DOI |
https://dx.doi.org/10.5492/wjccm.v8.i7.120 |
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