icd

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Tools for working with icd codes and comorbidities. This was inspired by the R package, icd <https://cran.r-project.org/web/packages/icd/index.html>_, as a simple python implementation for some of the base functionality. This has been benchmarked to be able to hand large datasets (tens of millions of rows) for various icd code manipulation tasks.

If you would be interested in helping contribute to this repository, feel free to send me an email <[email protected]>_.

Usage

Basic usage includes two very common tasks while dealing with icd code data.

• Transforming datasets from a long to wide format
• Processing icd codes for known comorbidity mappings

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Transforming from long to wide

Data is commonly in a long format that may have a key for an individual such as person_id with many claims claim_id belonging to it.

For example:

+------------+------------+-----------+------------+------------+ | claim_id | person_id | icd_cd_1 | icd_cd_2 | icd_cd_3 | +============+============+===========+============+============+ | 001 | A | code_6 | code_2 | | +------------+------------+-----------+------------+------------+ | 002 | A | code_8 | | | +------------+------------+-----------+------------+------------+ | 003 | A | code_3 | code_2 | code_6 | +------------+------------+-----------+------------+------------+ | 004 | B | code_1 | | | +------------+------------+-----------+------------+------------+ | 005 | B | code_2 | code_3 | | +------------+------------+-----------+------------+------------+ | 006 | C | code_4 | code_2 | code_5 | +------------+------------+-----------+------------+------------+

For easier processing, we must transform the table into a more collapsed version. The number of icd columns then becomes the maximum unique codes for any given person_id.

Such as:

+------------+-----------+------------+------------+------------+ | person_id | icd_cd_1 | icd_cd_2 | icd_cd_3 | icd_cd_4 | +============+===========+============+============+============+ | A | code_6 | code_2 | code_8 | code_3 | +------------+-----------+------------+------------+------------+ | B | code_1 | code_2 | code_3 | | +------------+-----------+------------+------------+------------+ | C | code_4 | code_2 | code_5 | | +------------+-----------+------------+------------+------------+

To accomplish this task, simply use the function long_to_short_transformation as such:

.. code-block:: python

import pandas as pd import icd

data = {"person_id":[1,1,1,2,2,3], "dx_1":["F11","E40","","F32","C77","G10"], "dx_2":["F1P","E400","","F322","C737",""]} df = pd.DataFrame.from_dict(data) icd.long_to_short_transformation(df,"person_id",["dx_1","dx_2"])

Where df is your pandas dataframe, "person_id" is the column you want to roll up on, and ["dx_1","dx_2"] is the array of columns that contain icd codes.

It is important to note that even if you only have one icd column, it must still be an array. Also, you must impute NaN values to be an empty string such as "".

The function will return a new dataframe with index of person_id, a column of person_id, as well as as many unique columns as needed in the following form icd_0, icd_1, ... , icd_n.

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Processing icd codes to known comorbidities

The second task has to do with actually mapping comorbidities to these icd codes. For this, you can use the function icd_to_comorbidities. This can be seen from going from a table of the format:

+------------+-----------+------------+------------+------------+ | person_id | icd_cd_1 | icd_cd_2 | icd_cd_3 | icd_cd_4 | +============+===========+============+============+============+ | A | code_6 | code_2 | code_8 | code_3 | +------------+-----------+------------+------------+------------+ | B | code_1 | code_2 | code_3 | | +------------+-----------+------------+------------+------------+ | C | code_4 | code_2 | code_5 | | +------------+-----------+------------+------------+------------+

To the format:

+------------+-----------+------------+------------+------------+ | person_id | comorb_1 | comorb_2 | comorb_3 | comorb_4 | +============+===========+============+============+============+ | A | True | False | True | True | +------------+-----------+------------+------------+------------+ | B | False | True | False | False | +------------+-----------+------------+------------+------------+ | C | False | False | False | False | +------------+-----------+------------+------------+------------+

This comorbidity mapping is pending on the mapping used.

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An example of doing is is carried out as such:

.. code-block:: python

import pandas as pd import icd

df = pd.DataFrame.from_dict({'icd_0': {1: 'F1P', 2: 'F322', 3: ''}, 'icd_1': {1: 'F11', 2: 'C77', 3: 'G10'}, 'icd_2': {1: '', 2: 'C737', 3: ''}, 'icd_3': {1: 'E400', 2: 'F32', 3: ''}, 'icd_4': {1: 'E40', 2: '', 3: ''}, 'person_id': {1: 1, 2: 2, 3: 3}}) icd.icd_to_comorbidities(df, "person_id", ["icd_0","icd_1","icd_2","icd_3","icd_4"])

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The default default mapping is the quan_elixhauser10, which is a transcription by Quan of the original Elixhauser icd 9 comorbidities in the following paper <https://www.ncbi.nlm.nih.gov/pubmed/16224307>_.

Optionally, you can provide a mapping keyword argument as such:

.. code-block:: python

icd.icd_to_comorbidities(df, "person_id", ["icd_0","icd_1","icd_2","icd_3","icd_4"], mapping="quan_elixhauser10")

The currently supported mappings are the default "quan_elixhauser10" as well as the "charlson10" mapping as referenced from the same paper above. Additionally, you can find them laid out in SAS code here <http://web.archive.org/web/20110225042437/http://www.chaps.ucalgary.ca/sas>_.

If you want to to create a custom comborbidity mapping, simply pass in a dict for the mapping argument instead of a supported keyword string. The dict must follow the following format as such:

.. code-block:: python

custom_mapping = {"paraplegia_and_hemiplegia":['G81','G82','G041','G114','G801','G802','G830','G831','G832','G833','G834','G839'], "renal_disease":['N18','N19','N052','N053','N054','N055','N056','N057','N250','I120','I131','N032','N033','N034','N035','N036','N037','Z490','Z491','Z492','Z940','Z992'], "cancer":['C00','C01','C02','C03','C04','C05','C06','C07','C08','C09','C10','C11','C12','C13','C14','C15','C16','C17','C18','C19','C20','C21','C22','C23','C24','C25','C26','C30','C31','C32','C33','C34','C37','C38','C39','C40','C41','C43','C45','C46','C47','C48','C49','C50','C51','C52','C53','C54','C55','C56','C57','C58','C60','C61','C62','C63','C64','C65','C66','C67','C68','C69','C70','C71','C72','C73','C74','C75','C76','C81','C82','C83','C84','C85','C88','C90','C91','C92','C93','C94','C95','C96','C97'], "moderate_or_sever_liver_disease":['K704','K711','K721','K729','K765','K766','K767','I850','I859','I864','I982'], "metastitic_carcinoma":['C77','C78','C79','C80'], "aids_hiv":['B20','B21','B22','B24'] } icd.icd_to_comorbidities(df, "person_id", ["icd_0","icd_1","icd_2","icd_3","icd_4"], mapping=custom_mapping)

The above function returns a new DataFrame with the person_id values as the index, a column of whatever "person_id" string is passed in, along with a column for every comorbidity populated with either True or False.

Installation

icd can easily be downloaded from Pypi package index via the following:

.. code-block:: python

pip install icd

Requirements ^^^^^^^^^^^^

• pandas <https://github.com/pandas-dev/pandas>_

Compatibility

icd currently supports Python 3.4, 3.5, and 3.6

Licence

MIT <https://github.com/mark-hoffmann/icd/blob/master/LICENSE.txt>_

Authors

icd was written by Mark Hoffmann <[email protected]>_.