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bloatectomy-0.0.9
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مشاهده بیشترتوضیحات
Bloatectomy: a method for the identification and removal of duplicate text in the bloated notes of electronic health records and other documents.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | bloatectomy-0.0.9 |
| نام | bloatectomy |
| نسخه کتابخانه | 0.0.9 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Summer Rankin, Roselie Bright, Katherine Dowdy |
| ایمیل نویسنده | summerKRankin@gmail.com |
| آدرس صفحه اصلی | https://github.com/MIT-LCP/bloatectomy |
| آدرس اینترنتی | https://pypi.org/project/bloatectomy/ |
| مجوز | GPLv3 |
# Bloatectomy
Bloatectomy: a method for the identification and removal of duplicate text in the bloated notes of electronic health records and other documents. Takes in a list of notes or a single file (.docx, .txt, .rtf, etc) or single string to be marked for duplicates. Marked output and tokens are output.
# Requirements
- Python>=3.7.x (in order for the regular expressions to work correctly)
- re
- sys
- pandas (optional, only necessary if using MIMIC III data)
- docx (optional, only necessary if input or output is a word/docx file)
# Installation
using anaconda or miniconda
```
conda install -c summerkrankin bloatectomy
```
using pip via PyPI
make sure to install it to python3 if your default is python2
```
python3 -m pip install bloatectomy
```
using pip via github
```
python3 -m pip install git+git://github.com/MIT-LCP/bloatectomy
```
manual install by cloning the repository
```
git clone git://github.com/MIT-LCP/bloatectomy
cd bloatectomy
python3 setup.py install
```
# Examples
To run bloatectomy on a sample string with the following options:
- highlighting duplicates
- display raw results
- output file as html
- output file of numbered tokens:
```
from bloatectomy import bloatectomy
text = '''Assessment and Plan
61 yo male Hep C cirrhosis
Abd pain:
-other labs: PT / PTT / INR:16.6// 1.5, CK / CKMB /
ICU Care
-other labs: PT / PTT / INR:16.6// 1.5, CK / CKMB /
Assessment and Plan
'''
bloatectomy(text, style='highlight', display=True, filename='sample_txt_highlight_output', output='html', output_numbered_tokens=True)
```
To use with example text or load ipynb examples, download the repository or just the bloatectomy_examples folder
```
cd bloatectomy_examples
from bloatectomy import bloatectomy
bloatectomy('./input/sample_text.txt',
style='highlight', display=False,
filename='./output/sample_txt_highlight_output',
output='html',
output_numbered_tokens=True,
output_original_tokens=True)
```
# Documentation
The paper is located at TBA
```
class bloatectomy(input_text,
path = '',
filename='bloatectomized_file',
display=False,
style='highlight',
output='html',
output_numbered_tokens=False,
output_original_tokens=False,
regex1=r"(.+?\.[\s\n]+)",
regex2=r"(?=\n\s*[A-Z1-9#-]+.*)",
postgres_engine=None,
postgres_table=None)
```
## Parameters
**input_text**: file, str, list
An input document (.txt, .rtf, .docx), a string of text, or list of hadm_ids for postgres mimiciii database or the raw text.
**style**: str, optional, default=`highlight`
Method for denoting a duplicate. The following are allowed: `highlight`, `bold`, `remov`.
**filename**: str, optional, default=`bloatectomized_file`
A string to name output file of the bloat-ectomized document.
**path**: str, optional, default=`' '`
The directory for output files.
**output_numbered_tokens**: bool, optional, default=`False`
If set to `True`, a .txt file with each token enumerated and marked for duplication, is output as `[filename]_token_numbers.txt`. This is useful when diagnosing your own regular expression for tokenization or testing the `remov` option for **style**.
**output_original_tokens**: bool, optional, default=`False`
If set to `True`, a .txt file with each original (non-marked) token enumerated but not marked for duplication, is output as `[filename]_original_token_numbers.txt`.
**display**: bool, optional, default=`False`
If set to `True`, the bloatectomized text will display in the console on completion.
**regex1**: str, optional, default=`r"(.+?\.[\s\n]+)"`
The regular expression for the first tokenization. Split on a period (.) followed by one or more white space characters (space, tab, line breaks) or a line feed character (`\n`). This can be replaced with any valid regular expression to change the way tokens are created.
**regex2**: str, optional, default=`r"(?=\n\s*[A-Z1-9#-]+.*)"`
The regular expression for the second tokenization. Split on any newline character (`\n`) followed by an uppercase letter, a number, or a dash. This can be replaced with any valid regular expression to change how sub-tokens are created.
**postgres_engine**: str, optional
The postgres connection. Only relevant for use with the MIMIC III dataset. When data is pulled from postgres the hadm_id of the file will be appended to the `filename` if set or the default `bloatectomized_file`. See the jupyter notebook [mimic_bloatectomy_example](./bloatectomy_examples/mimic_bloatectomy_example.ipynb) for the example code.
**postgres_table**: str, optional
The name of the postgres table containing the concatenated notes. Only relevant for use with the MIMIC III dataset. When data is pulled from postgres the hadm_id of the file will be appended to the `filename` if set or the default `bloatectomized_file`. See the jupyter notebook [mimic_bloatectomy_example](./bloatectomy_examples/mimic_bloatectomy_example.ipynb) for the example code.
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl bloatectomy-0.0.9:
pip install bloatectomy-0.0.9.whl
نصب پکیج tar.gz bloatectomy-0.0.9:
pip install bloatectomy-0.0.9.tar.gz