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false-alarm-reduction

Code for building a model to reduce false alarms in the intensive care unit

https://github.com/mit-lcp/false-alarm-reduction

Science Score: 23.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
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    DOI references
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    Academic publication links
    Links to: zenodo.org
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  • ○
    Scientific vocabulary similarity
    Low similarity (1.9%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Code for building a model to reduce false alarms in the intensive care unit

Basic Info
  • Host: GitHub
  • Owner: MIT-LCP
  • License: mit
  • Language: Python
  • Default Branch: master
  • Size: 13.2 MB
Statistics
  • Stars: 2
  • Watchers: 4
  • Forks: 3
  • Open Issues: 1
  • Releases: 2
Created about 10 years ago · Last pushed about 8 years ago
Metadata Files
Readme Contributing License Codemeta

README.rst 

false-alarm-reduction
=====================

|DOI|

Code for building a model to reduce false alarms in the intensive care
unit.

.. |DOI| image:: https://zenodo.org/badge/59120353.svg
   :target: https://zenodo.org/badge/latestdoi/59120353

Owner

  • Name: MIT Laboratory for Computational Physiology
  • Login: MIT-LCP
  • Kind: organization
  • Location: Cambridge, MA

Research on improving health care through data analysis, including use of MIMIC-III and other data sources

CodeMeta (codemeta.json) 

{
  "@context": "https://raw.githubusercontent.com/codemeta/codemeta/master/codemeta.jsonld",
  "@type": "Code",
  "author": [
    {
      "@id": "http://orcid.org/0000-0001-8419-5527",
      "@type": "Person",
      "email": "liandrea@mit.edu",
      "name": "Andrea S. Li",
      "affiliation": "Massachusetts Institute of Technology"
    },
    {
      "@id": "http://orcid.org/0000-0002-8735-3014",
      "@type": "Person",
      "email": "aewj@mit.edu",
      "name": "Alistair E. W. Johnson",
      "affiliation": "Massachusetts Institute of Technology"
    },
    {
      "@id": "http://orcid.org/0000-0002-6318-2978",
      "@type": "Person",
      "email": "rgmark@mit.edu",
      "name": "Roger G. Mark",
      "affiliation": "Massachusetts Institute of Technology"
    }
  ],
  "identifier": "http://dx.doi.org/10.5281/zenodo.889036",
  "codeRepository": "https://github.com/MIT-LCP/false-alarm-reduction",
  "datePublished": "2017-09-11",
  "dateModified": "2017-09-11",
  "dateCreated": "2017-09-11",
  "description": "This code provides tools to decrease the false alarm rate for cardiac arrhythmias in the intensive care unit. It explores a baseline algorithm based on an algorithm published by Plesinger, et al. (2016), as well as dynamic time warping as a technique to identify false alarms.",
  "keywords": "false alarm reduction, signal processing, dynamic time warping, intensive care unit, arrhythmia",
  "license": "MIT",
  "title": "False alarm reduction in the intensive care unit",
  "version": "v1.0.1"
}

GitHub Events

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Last Year

Issues and Pull Requests

Last synced: over 1 year ago

All Time
  • Total issues: 1
  • Total pull requests: 1
  • Average time to close issues: 11 months
  • Average time to close pull requests: N/A
  • Total issue authors: 1
  • Total pull request authors: 1
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Top Authors
Issue Authors
  • alistairewj (1)
Pull Request Authors
  • stsievert (1)
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Dependencies

requirements.txt pypi
  • PeakUtils >=1.1.0
  • fastdtw >=0.3.2
  • matplotlib >=2.0.2
  • numpy >=1.13.1
  • pandas >=0.20.3
  • scikit-learn >=0.19.0
  • scipy >=0.19.0
  • sklearn >=0.0
  • spectrum >=0.7.1
  • virtualenv >=15.0.1
  • wfdb >=1.2.2
setup.py pypi
  • PeakUtils >=1.1.0
  • fastdtw >=0.3.2
  • matplotlib >=2.0.2
  • numpy >=1.13.1
  • pandas >=0.20.3
  • scikit-learn >=0.19.0
  • scipy >=0.19.0
  • sklearn >=0.0
  • spectrum >=0.7.1
  • wfdb ==1.2.2