FEASIBILITY ANALYSIS OF CLINICAL TRAIL

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

Clinical trial is the foundation pillar of the drug discovery process. Roughly 1 in 10 of drugs tested in human subjects receive FDA approval. Given the title and summary of the clinical trial, this tool will predict if the clinical trial will be approved or not. This tool can be used in the nascent stage of the clinical trial process which can help the researchers take a better decision. This tool is trained on around 1200 approved/rejected clinical trials.

2. Preparing CSV

The csv file used in the model has 2 columns ,

• Title
• Abstract

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 Feasibility Analysis

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

df = pd.read_csv('FeasibilityAnalysis.csv')
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 = [item.replace("'", "").replace('"', "") for item in sampleResponse]
sampleResponse