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

pip install bloatectomy

using pip via github

pip install git+git://github.com/MIT-LCP/mimic-code

manual install by cloning the repository

git clone git://github.com/MIT-LCP/mimic-code
cd bloatectomy
python3 setup.py install

Example

To run bloatectomy on the sample text provided in the input folder:

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

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. See the jupyter notebook mimic_bloatectomy_example 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. See the jupyter notebook mimic_bloatectomy_example for the example code.