Predicting Patient Eligibility for Cancer Trials Usecase

Contents

1. Introdution
2. Preparing CSV
3. Using End point
   • 3.1 Creating model
   • 3.2 Creating Endpoint Config
   • 3.3 Creating Endpoint
   • 3.4 Invoking Endpoint
   • 3.5. Deleting the Endpoint - Optional
4. Model Output

1. Introdution

Interventional cancer clinical trials are generally too restrictive, and some patients are often excluded on the basis of comorbidity, past or concomitant treatments, or the fact that they are over a certain age. Using Deep Neural Network, this tool will predict the eligibility of a patient for a cancer clinical trial after going through his/her diagnosis notes.

2. Preparing CSV

The csv file used in the model has 2 columns viz

• study_intervention
• diagnosis

Same kind of CSV file should be fed to the model for best output.

For reference, A sample CSV file is attached in the link. The user only needs to clik on the link to get it downloaded to the local. The data should be in a similar format like the sample csv.

Download sample Data from the link : Data Source for Patient Eligibility for Cancer Trials

import pandas as pd
df = pd.read_csv('S3/Sagemaker path of EligibilityForCancerTrials.csv file')
df.to_csv("sample.csv",index=False)
df.head(3)

3. Using Endpoint

The model can be directly accessed via the console provided by AWS. However, for a more customized process one can access the model using the below code as well.

3.1 Creating Model

To create a model, import boto3, sagemaker and get the arn of the model package

import boto3
import sagemaker
role = sagemaker.get_execution_role()
smmp = boto3.client('sagemakermp')
modelName='Name of the model'
modelArn = 'Model ARN name'
createHeatIndexResponse = smmp.create_model(ModelName=modelName,\
                             Containers=[{'ModelPackageName': modelArn}],\
                             ExecutionRoleArn=role,\
                             EnableNetworkIsolation=True )

3.2 Creating Endpoint Config

configName ='<Input Configuration Name>'
instanceType = '<Input Instance Type>'
createHeatIndexEndpointConfig = smmp.create_endpoint_config(EndpointConfigName = configName, ProductionVariants = [{'InstanceType':instanceType, 'InitialInstanceCount':1, 'ModelName':modelName, 'VariantName':'xyz'}])

3.3 Creating Endpoint

endpointName = '<Input Endpoint Name>'
createHeatIndexEndpoint = smmp.create_endpoint(EndpointName = endpointName, EndpointConfigName = configName)

3.4 Invoking Endpoint

runtime = boto3.Session().client('runtime.sagemaker')

#Reading Input Data 
with open('sample.csv','rb') as f:
    payload = f.read()

response = runtime.invoke_endpoint(EndpointName = endpointName, ContentType = 'text/csv', Body = payload)
result = response['Body'].read().decode()

#Writing Output Data 
with open('sampleOutput.txt','w') as f:
    f.write(result)

3.5. Deleting the Endpoint - Optional

If you're ready to be done with this notebook, please run the delete_endpoint line in the cell below. This will remove the hosted endpoint you created and avoid any charges from a stray instance being left on

sagemaker.Session().delete_endpoint(endpointName)

4. Model Output

with open("./sampleOutput.txt","r") as f:
    sampleResponse =f.read()
    sampleResponse = sampleResponse.split('\n')
sampleResponse