Patient Selection for Diabetes Drug Testing

I built a model that predicts the expected days of hospitalization time, which determines whether the patient is selected for the clinical trial. In clinical trials, the drug is often administered over a few days in the hospital with frequent monitoring/testing. Therefore, the target patients are people that are likely to be in the hospital for this duration of time and will not incur significant additional costs for administering the drug and monitoring after discharge.

Dataset

I used a modified dataset from UC Irvine.

Dependencies

Using Anaconda consists of the following:

1.Install miniconda on your computer, by selecting the latest Python version for your operating system. If you already have conda or miniconda installed, you should be able to skip this step and move on to step 2.

Download the latest version of miniconda that matches your system.

	Linux	Mac	Windows
64-bit	64-bit (bash installer)	64-bit (bash installer)	64-bit (exe installer)
32-bit	32-bit (bash installer)		32-bit (exe installer)

Install miniconda on your machine. Detailed instructions:

• Linux: http://conda.pydata.org/docs/install/quick.html#linux-miniconda-install
• Mac: http://conda.pydata.org/docs/install/quick.html#os-x-miniconda-install
• Windows: http://conda.pydata.org/docs/install/quick.html#windows-miniconda-install

2.Create and activate a new conda environment.

For Windows users, these following commands need to be executed from the Anaconda prompt as opposed to a Windows terminal window. For Mac, a normal terminal window will work.

These instructions also assume you have git installed for working with Github from a terminal window, but if you do not, you can download that first with the command:

conda install git

3.Create local environment

• Clone the repository, and navigate to the downloaded folder. This may take a minute or two to clone due to the included image data.

git clone https://github.com/iDataist/Patient-Selection-for-Diabetes-Drug-Testing.git
cd Downloads

• Create (and activate) a new environment, named ehr-env with Python 3.7. If prompted to proceed with the install (Proceed [y]/n) type y.

  • Linux or Mac:

   conda create -n ehr-env python=3.7
   source activate ehr-env
  

  • Windows:

   conda create --name ehr-env python=3.7
   activate ehr-env
  

  At this point your command line should look something like: (ehr-env) <User>:USER_DIR <user>$. The (ehr-env) indicates that your environment has been activated, and you can proceed with further package installations.

• Install a few required pip packages, which are specified in the requirements text file. Be sure to run the command from the project root directory since the requirements.txt file is there.

pip install -r requirements.txt
ipython3 kernel install --name ehr-env --user