Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives.
Saved in:
| Title: | Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives. |
|---|---|
| Authors: | Gehrmann S; MIT Critical Data, Laboratory for Computational Physiology, Cambridge, MA, United States of America.; Harvard SEAS, Harvard University, Cambridge, MA, United States of America., Dernoncourt F; MIT Critical Data, Laboratory for Computational Physiology, Cambridge, MA, United States of America.; Massachusetts Institute of Technology, Cambridge, MA, United States of America.; Adobe Research, San Jose, CA, United States of America., Li Y; MIT Critical Data, Laboratory for Computational Physiology, Cambridge, MA, United States of America.; Harvard T.H. Chan School of Public Health, Cambridge, MA, United States of America., Carlson ET; MIT Critical Data, Laboratory for Computational Physiology, Cambridge, MA, United States of America.; Philips Research North America, Cambridge, MA, United States of America., Wu JT; MIT Critical Data, Laboratory for Computational Physiology, Cambridge, MA, United States of America.; Harvard T.H. Chan School of Public Health, Cambridge, MA, United States of America., Welt J; MIT Critical Data, Laboratory for Computational Physiology, Cambridge, MA, United States of America.; Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA, United States of America., Foote J Jr; MIT Critical Data, Laboratory for Computational Physiology, Cambridge, MA, United States of America.; Tufts University School of Medicine, Cambridge, MA, United States of America., Moseley ET; MIT Critical Data, Laboratory for Computational Physiology, Cambridge, MA, United States of America.; College of Science and Mathematics, University of Massachusetts, Boston, MA, United States of America., Grant DW; MIT Critical Data, Laboratory for Computational Physiology, Cambridge, MA, United States of America.; Department of Surgery, Division of Plastic and Reconstructive Surgery, Washington University School of Medicine, St. Louis, MO, United States of America., Tyler PD; MIT Critical Data, Laboratory for Computational Physiology, Cambridge, MA, United States of America.; Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States of America., Celi LA; MIT Critical Data, Laboratory for Computational Physiology, Cambridge, MA, United States of America.; Massachusetts Institute of Technology, Cambridge, MA, United States of America. |
| Source: | PloS one [PLoS One] 2018 Feb 15; Vol. 13 (2), pp. e0192360. Date of Electronic Publication: 2018 Feb 15 (Print Publication: 2018). |
| Publication Type: | Comparative Study; Journal Article; Research Support, Non-U.S. Gov't |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
|
Full text is not displayed to guests.
Login for full access.
|
|
| ISSN: | 1932-6203 |
|---|---|
| DOI: | 10.1371/journal.pone.0192360 |
