Skip to content

Latest commit

 

History

History

docs

Folders and files

NameName
Last commit message
Last commit date

parent directory

..
 
 
 
 
 
 
 
 
 
 
home heroImage actionText actionLink footer
true
/hero.png
Get Started →
/usage/

EHR Functions

A library of useful EHR related functions.

About

This is a library of useful EHR functions for use within Python, especially when using Jupyter notebooks.

When utilizing Jupyter notebooks for processing data and training models I found myself copying the same code between notebooks. This code consisted of steps to split my data, create a model, compute some metrics, etc.; and thus the notebooks became very long with little focus on the actual analysis. Therefore, this set of functions were created to allow for a focus on analysis and to abstract away the process of cleaning data and running models.

::: warning The documentation is still being written out so a lot of sections are left blank. :::

Example

from ehr_functions.features import demographics
import pandas as pd 

df = pd.DataFrame({
    'PatientID': [1, 2, 3, 4],
    'PatientAge': [21, 35, 27, 24],
    'PatientGender': ['M', 'F', 'M', 'F'],
    'PatientCategory': ['A', 'B', 'C', 'A'],
})

dems = demographics.get_features(df)
print(dems.head())
PatientID PatientAge PatientGender PatientCategory_A PatientCategory_B PatientCategory_C
1 21 1 1 0 0
2 35 0 0 1 0
3 27 1 0 0 1
4 24 0 1 0 0