https://github.com/zhijing-jin/palliative-care-topic-modeling

Topic models to analyze journals on palliative care
https://github.com/zhijing-jin/palliative-care-topic-modeling

Last synced: 10 days ago
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Topic models to analyze journals on palliative care

• Host: GitHub
• URL: https://github.com/zhijing-jin/palliative-care-topic-modeling
• Owner: zhijing-jin
• License: mit
• Created: 2021-11-03T00:37:54.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2024-01-01T22:00:53.000Z (about 1 year ago)
• Last Synced: 2024-11-16T04:00:10.734Z (2 months ago)
• Language: Python
• Size: 2.36 MB
• Stars: 3
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
This repo stores the codes for topic modeling on palliative care journals for the following paper:

[**Revelations from a Machine Learning Analysis of the Most Downloaded Articles Published in Journal of Palliative Medicine 1999–2018**](https://www.liebertpub.com/doi/full/10.1089/jpm.2022.0574) (Journal of Palliative Medicine, 2023) by *Suzanne Tamang, Zhijing Jin, Vyjeyanthi S Periyakoil*.

## Data Preparation

First, download the journal papers. For convenience, you can check the `papers_parsed/` folder, or you can also download the data on your own as follows.

```bash

bash 1_download_pdfs.sh # To download papers from the journal `jpm`

bash 1_download_pdfs_jwh.sh # To download papers from the journal `jwh`

```

## Environment Setup

Install all the necessary python packages.

```bash

bash 2_pdf2json_prep_env.sh

```

## How to Run the Topic Model

Run the topic modeling on the default journal `jpm`:

```bash

python 3_pdf2json2topics.py

```

Or you can also run the topic modeling on the other journal `jwh`:

```bash

python 3_pdf2json2topics.py -journal_name jwh

```

In addition, we also saved the text for word cloud generation in the `outputs/` folder.

## Citation

```bib

@article{tamang2023revelations,

    author = "Tamang, Suzanne and Jin, Zhijing and Periyakoil, Vyjeyanthi S.",

    title = "Revelations from a Machine Learning Analysis of the Most Downloaded Articles Published in Journal of Palliative Medicine 1999--2018",

    journal = "Journal of Palliative Medicine",

    volume = "26",

    number = "1",

    pages = "13-16",

    year = "2023",

    doi = "10.1089/jpm.2022.0574",

    note = "PMID: 36607778",

    URL = "https://doi.org/10.1089/jpm.2022.0574",

    eprint = "https://doi.org/10.1089/jpm.2022.0574"

}

```