Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Tristan Naumann"'
Autor:
Aparna Balagopalan, Ioana Baldini, Leo Anthony Celi, Judy Gichoya, Liam G McCoy, Tristan Naumann, Uri Shalit, Mihaela van der Schaar, Kiri L Wagstaff
Publikováno v:
PLOS Digital Health, Vol 3, Iss 4, p e0000474 (2024)
Despite significant technical advances in machine learning (ML) over the past several years, the tangible impact of this technology in healthcare has been limited. This is due not only to the particular complexities of healthcare, but also due to str
Externí odkaz:
https://doaj.org/article/f6741c27e30c4088ab2b333f2aee7bf5
Autor:
E. Hope Weissler, Tristan Naumann, Tomas Andersson, Rajesh Ranganath, Olivier Elemento, Yuan Luo, Daniel F. Freitag, James Benoit, Michael C. Hughes, Faisal Khan, Paul Slater, Khader Shameer, Matthew Roe, Emmette Hutchison, Scott H. Kollins, Uli Broedl, Zhaoling Meng, Jennifer L. Wong, Lesley Curtis, Erich Huang, Marzyeh Ghassemi
Publikováno v:
Trials, Vol 22, Iss 1, Pp 1-15 (2021)
Abstract Background Interest in the application of machine learning (ML) to the design, conduct, and analysis of clinical trials has grown, but the evidence base for such applications has not been surveyed. This manuscript reviews the proceedings of
Externí odkaz:
https://doaj.org/article/3b9a18a768594d159cdbecc73cb339d5
Autor:
E. Hope Weissler, Tristan Naumann, Tomas Andersson, Rajesh Ranganath, Olivier Elemento, Yuan Luo, Daniel F. Freitag, James Benoit, Michael C. Hughes, Faisal Khan, Paul Slater, Khader Shameer, Matthew Roe, Emmette Hutchison, Scott H. Kollins, Uli Broedl, Zhaoling Meng, Jennifer L. Wong, Lesley Curtis, Erich Huang, Marzyeh Ghassemi
Publikováno v:
Trials, Vol 22, Iss 1, Pp 1-1 (2021)
Externí odkaz:
https://doaj.org/article/53c3912e97654cddaa911b0e78b60d03
Autor:
Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L Beam, Irene Y Chen, Rajesh Ranganath
Publikováno v:
The Lancet: Digital Health, Vol 1, Iss 4, Pp e157-e159 (2019)
Externí odkaz:
https://doaj.org/article/faa4d4b3dae14f5fb7794f2772a1bc08
Autor:
Sam Preston, Mu Wei, Rajesh Rao, Robert Tinn, Naoto Usuyama, Michael Lucas, Yu Gu, Roshanthi Weerasinghe, Soohee Lee, Brian Piening, Paul Tittel, Naveen Valluri, Tristan Naumann, Carlo Bifulco, Hoifung Poon
Publikováno v:
Patterns. 4:100726
Autor:
Benedikt Boecking, Naoto Usuyama, Shruthi Bannur, Daniel C. Castro, Anton Schwaighofer, Stephanie Hyland, Maria Wetscherek, Tristan Naumann, Aditya Nori, Javier Alvarez-Valle, Hoifung Poon, Ozan Oktay
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031200588
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::907f669f41e07274f962a309a9855503
https://doi.org/10.1007/978-3-031-20059-5_1
https://doi.org/10.1007/978-3-031-20059-5_1
Autor:
Khader Shameer, Faisal Khan, Olivier Elemento, Tomas Andersson, Scott H. Kollins, Zhaoling Meng, Daniel F. Freitag, Rajesh Ranganath, Lesley H. Curtis, Michael C. Hughes, E. Hope Weissler, James Benoit, Yuan Luo, Matthew T. Roe, Uli C. Broedl, Tristan Naumann, Jennifer L. Wong, Erich Huang, Marzyeh Ghassemi, Emmette Hutchison, Paul Slater
Publikováno v:
Trials, Vol 22, Iss 1, Pp 1-15 (2021)
Trials
Trials
Background Interest in the application of machine learning (ML) to the design, conduct, and analysis of clinical trials has grown, but the evidence base for such applications has not been surveyed. This manuscript reviews the proceedings of a multi-s
Publikováno v:
IEEE/ACM Transactions on Computational Biology and Bioinformatics. 16:139-153
This article reviews recent advances in applying natural language processing (NLP) to Electronic Health Records (EHRs) for computational phenotyping. NLP-based computational phenotyping has numerous applications including diagnosis categorization, no
Autor:
Robert Tinn, Chenyan Xiong, Tristan Naumann, Cliff Wong, Jianfeng Gao, Hao Cheng, Naoto Usuyama, Richard Rogahn, Hoifung Poon, Jinchao Li, Yu Wang, Eric Horvitz, Zhihong Shen, Yang Qin, Paul N. Bennett
Information overload is a prevalent challenge in many high-value domains. A prominent case in point is the explosion of the biomedical literature on COVID-19, which swelled to hundreds of thousands of papers in a matter of months. In general, biomedi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::62f75653374449dd927ef8613c9b3095
Autor:
Hao Cheng, Naoto Usuyama, Yu Gu, Hoifung Poon, Jianfeng Gao, Michael Lucas, Robert Tinn, Tristan Naumann, Xiaodong Liu
Publikováno v:
BASE-Bielefeld Academic Search Engine
Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing assumption
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1fdf757b4e15c0aabcfd010c6f6d3936
http://arxiv.org/abs/2007.15779
http://arxiv.org/abs/2007.15779