Metadata-Version: 2.1
Name: bloatectomy
Version: 0.0.9
Summary: Bloatectomy: a method for the identification and removal of duplicate text in the bloated notes of electronic health records and other documents.
Home-page: https://github.com/MIT-LCP/mimic-code
Author: Summer Rankin, Roselie Bright, Katherine Dowdy
Author-email: summerKRankin@gmail.com
License: GPLv3
Keywords: python,medical informatics,electronic health records,electronic medical records,public health informatics,clinical information extraction,informatics,natural language processing
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Text Processing
Classifier: Topic :: Utilities
Requires-Python: >=3.7
Description-Content-Type: text/markdown

# 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 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/mimic-code TBA
```
manual install by cloning the repository
```
git clone git://github.com/MIT-LCP/mimic-code TBA
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 using this option, do not invoke a `filename` and it will name each file with the hadm_id. 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 using this option, do not invoke a `filename` and it will name each file with the hadm_id. See the jupyter notebook [mimic_bloatectomy_example](./bloatectomy_examples/mimic_bloatectomy_example.ipynb) for the example code.


