When referencing MIMIC-III, please cite the following publication:
Please also include the standard PhysioNet citation:
Word vectors corresponding to the AMIA 2018 Informatics Summit paper of the same name. As described in the paper, these word vectors are trained with [word2vec](https://github.com/tmikolov/word2vec) using hyperparameters suggested by Levy et al.: 300-dimensional SGNS with 10 negative samples, a min-count of 10, a subsampling rate of 1e-5, and a 10-word window.
Derived Data
The data associated with this repository is available here: https://physionet.org/works/MIMICIIIDerivedDataRepository/files/approved/what-is-in-a-note
Contribution
Contributed on 2018-01-08 by Tristan Naumann
Source Controlled Code
Source Controlled Code Location: https://github.com/wboag/wian
Name Last modified Size Description
Parent Directory - DOI 08-Aug-2018 19:55 21 build_word2vec_corpus.py.gz 09-Jan-2018 00:35 1.8K Python source file
If you would like help understanding, using, or downloading content, please see our Frequently Asked Questions. If you have any comments, feedback, or particular questions regarding this page, please send them to the webmaster. Comments and issues can also be raised on PhysioNet's GitHub page. Updated Friday, 28-Oct-2016 22:58:42 CEST |
PhysioNet is supported by the National Institute of General Medical Sciences (NIGMS) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under NIH grant number 2R01GM104987-09.
|