Skip to content

cdcai/enriched-LSTMs

 
 

Repository files navigation

enriched_LSTMs

Getting more out of LSTMs for classifying multimodal health data

Overview

Background

Our data

We used emergency department (ED) visit record data to develop our models. The records had one free-text field, chief complaint, along with a number of other discrete variables, like age group, sex, mode of arrival, and hospital code. This is the third project we've done with the data, so if you're interested in learning more about them, check out our papers about using them for classifying chief complaints and for generating synthetic chief complaints.

Our results

Code

Example preprocessing run:

python preprocessing.py ^
--data_dir=C:/data/syndromic/ ^
--input_file=sample.csv ^
--file_type=csv ^
--text_column=cc ^
--clean_text=True ^
--convert_numerals=True ^ 
--target_column=ccs

And an example training and test run:

python train_and_test.py ^
--data_dir=C:/data/syndromic/ ^ 
--text_file=word_sents.hdf5 ^
--records_npz=sparse_records.npz ^ 
--records_csv=sample.csv ^
--target_column=ccs ^
--patience=1

Technical requirements

We did all

Public Domain Standard Notice

This repository constitutes a work of the United States Government and is not subject to domestic copyright protection under 17 USC § 105. This repository is in the public domain within the United States, and copyright and related rights in the work worldwide are waived through the CC0 1.0 Universal public domain dedication. All contributions to this repository will be released under the CC0 dedication. By submitting a pull request you are agreeing to comply with this waiver of copyright interest.

License Standard Notice

The repository utilizes code licensed under the terms of the Apache Software License and therefore is licensed under ASL v2 or later.

This source code in this repository is free: you can redistribute it and/or modify it under the terms of the Apache Software License version 2, or (at your option) any later version.

This source code in this repository is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the Apache Software License for more details.

You should have received a copy of the Apache Software License along with this program. If not, see http://www.apache.org/licenses/LICENSE-2.0.html

The source code forked from other open source projects will inherit its license.

Privacy Standard Notice

This repository contains only non-sensitive, publicly available data and information. All material and community participation is covered by the Disclaimer and Code of Conduct. For more information about CDC's privacy policy, please visit http://www.cdc.gov/privacy.html.

Contributing Standard Notice

Anyone is encouraged to contribute to the repository by forking and submitting a pull request. (If you are new to GitHub, you might start with a basic tutorial.) By contributing to this project, you grant a world-wide, royalty-free, perpetual, irrevocable, non-exclusive, transferable license to all users under the terms of the Apache Software License v2 or later.

All comments, messages, pull requests, and other submissions received through CDC including this GitHub page are subject to the Presidential Records Act and may be archived. Learn more at http://www.cdc.gov/other/privacy.html.

Records Management Standard Notice

This repository is not a source of government records, but is a copy to increase collaboration and collaborative potential. All government records will be published through the CDC web site.

Additional Standard Notices

Please refer to CDC's Template Repository for more information about contributing to this repository, public domain notices and disclaimers, and code of conduct.

Releases

No releases published

Packages

No packages published

Languages

  • Python 100.0%