https://github.com/andreped/adverse-events

IEEE BIBM 2021: Bayesian optimization-guided topic modeling for automatic detection of sepsis-related events from free text
https://github.com/andreped/adverse-events

adverse-events bayesian-optimization classification data-set detection ieee-bibm latent-dirichlet-allocation lda machine-learning natural-language-processing sepsis

Last synced: about 2 months ago
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IEEE BIBM 2021: Bayesian optimization-guided topic modeling for automatic detection of sepsis-related events from free text

• Host: GitHub
• URL: https://github.com/andreped/adverse-events
• Owner: andreped
• License: mit
• Created: 2021-05-10T10:51:53.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2023-04-16T19:59:56.000Z (over 1 year ago)
• Last Synced: 2024-05-21T06:14:18.154Z (7 months ago)
• Topics: adverse-events, bayesian-optimization, classification, data-set, detection, ieee-bibm, latent-dirichlet-allocation, lda, machine-learning, natural-language-processing, sepsis
• Language: Python
• Homepage:
• Size: 73.2 KB
• Stars: 7
• Watchers: 5
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# adverse-events

[![license](https://img.shields.io/github/license/DAVFoundation/captain-n3m0.svg?style=flat-square)](https://github.com/DAVFoundation/captain-n3m0/blob/master/LICENSE)

This repository contains the source code related to the manuscript _"Preliminary Processing and Analysis of an Adverse Event Dataset for Detecting Sepsis-Related Events"_, presented at the [IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2021)](http://ieeebibm.org/BIBM2021/).

A PDF of the published paper can be accessed [here](https://ntnuopen.ntnu.no/ntnu-xmlui/bitstream/handle/11250/2979827/B579_9996.pdf?sequence=2&isAllowed=y). See [here](https://github.com/andreped/adverse-events/releases/tag/v1.0) to download the exact version of the source code used in the publication (v1.0).

## [Usage](https://github.com/andreped/adverse-events#usage)

1) Clone repo:

    > git clone https://github.com/andreped/adverse-events.git

2) Create virtual environment, activate it, and install dependencies:

    > cd adverse-events/python  

    > virtualenv -ppython3 venv --clear  

    > source venv/bin/activate  

    > pip install -r /path/to/requirements.txt

3) Create the project structure as defined [below](https://github.com/andreped/adverse-events#project-structure).

4) Run scripts for training and evaluating different classifier models:

    > python main.py misc/default-params.ini

Different parameters relevant for the analysis, building of models, evaluation, plotting results, and similar, may be modified in the INI-file.

## [Project structure](https://github.com/andreped/adverse-events#project-structure)

    └── adverse-events

        ├── python

        │   ├── multi-class

        │   ├── topic-analysis

        │   ├── utils

        │   └── ...

        ├── data

        │   ├── EQS_files

        │   ├── file-with-all-notes.csv

        │   └── file_with_annotated_notes.csv

        └── output

            ├── history

            ├── models

            └── figures

## [How to cite](https://github.com/andreped/adverse-events#how-to-cite)

If you use parts of the source code in your research, please, cite this publication:

```

@INPROCEEDINGS{yan2021sepsis,

    author={Yan, Melissa Y. and Høvik, Lise Husby and Pedersen, André and Gustad, Lise Tuset and Nytrø, Øystein},

    booktitle={2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},

    title={Preliminary Processing and Analysis of an Adverse Event Dataset for Detecting Sepsis-Related Events},

    year={2021},

    pages={1605-1610},

    doi={10.1109/BIBM52615.2021.9669410}

}

```