sunnypig618 / healthcareai-py

Python tools for healthcare machine learning

healthcareai

The aim of healthcareai is to streamline machine learning in healthcare. The package has two main goals:

• Allow one to easily create models based on tabular data, and deploy a best model that pushes predictions to SQL Server.
• Provide tools related to data cleaning, manipulation, and imputation.

Installation

• Using Windows

  • If you haven't, install 64-bit Python 3.5 via the Anaconda distribution

  • Open the terminal (ie git bash, if using Windows)
  • run conda install pyodbc
  • run pip install https://github.com/HealthCatalystSLC/healthcareai-py/zipball/master

• Using Linux / OSX (via docker)

  • Install docker

  • Clone this repo (look for the green button on the repo main page)
  • cd into the cloned directory
  • run docker build -t healthcareai .
  • run the docker instance with docker run -p 8888:8888 healthcareai
  • You should then have a jupyter notebook available on http://localhost:8888.

Getting started

• Visit healthcare.ai to read the docs and find examples. Also, see this notebook.
• Modify the queries and parameters to match your data
• If you plan on deploying a model (ie, pushing predictions to SQL Server), run this in SSMS beforehand:

CREATE TABLE [SAM].[dbo].[HCPyDeployClassificationBASE] (
    [BindingID] [int] ,
    [BindingNM] [varchar] (255),
    [LastLoadDTS] [datetime2] (7),
    [PatientEncounterID] [decimal] (38, 0), --< change to your grain col
    [PredictedProbNBR] [decimal] (38, 2),
    [Factor1TXT] [varchar] (255),
    [Factor2TXT] [varchar] (255),
    [Factor3TXT] [varchar] (255))

CREATE TABLE [SAM].[dbo].[HCPyDeployRegressionBASE] (
    [BindingID] [int],
    [BindingNM] [varchar] (255),
    [LastLoadDTS] [datetime2] (7),
    [PatientEncounterID] [decimal] (38, 0), --< change to your grain col
    [PredictedValueNBR] [decimal] (38, 2),
    [Factor1TXT] [varchar] (255),
    [Factor2TXT] [varchar] (255),
    [Factor3TXT] [varchar] (255))

Note that we're currently working on easy connections to other types of databases.

Contributing

We welcome community contributions. See here to get started!

Documentation

To render docs, create a virtualenvironment for hcpytools

• cd to directory where folder was downloaded
• Type python -m virtualenv healthcare

Install required python modules

• Type pip install -r dev-requirements.txt.

For Windows

• Run sphinx-autobuild docs docs/_build/html in the root of the repo
• Open a browser to http://127.0.0.1:8000

For non-Windows:

• Simply run inv docs and a new browser window should open to http://127.0.0.1:8001