Contents
An adverse drug reaction (ADR) is an injury caused by taking medication. ADRs may occur following a single dose or prolonged administration of a drug or result from the combination of two or more drugs. When patients suffer unintended reactions to medicines, it can be both dangerous for the individual and costly to society. Using ML models, this tool would help medical professionals to forecast adverse drug reactions (ADRs) and minimise risks to patients. Model used is Random Forest( One vs Rest Classifier).
The csv file used in the model has 13 columns ,
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 Adverse Drug Reaction
import pandas as pd
df = pd.read_csv('S3/Sagemaker path of AdverseDrugReaction.csv file')
df.to_csv("sample.csv",index=False)
df.head(3)
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.
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 )
configName ='<Input Configuration Name>'
instanceType = '<Input Instance Type>'
createHeatIndexEndpointConfig = smmp.create_endpoint_config(EndpointConfigName = configName, ProductionVariants = [{'InstanceType':instanceType, 'InitialInstanceCount':1, 'ModelName':modelName, 'VariantName':'xyz'}])
endpointName = '<Input Endpoint Name>'
createHeatIndexEndpoint = smmp.create_endpoint(EndpointName = endpointName, EndpointConfigName = configName)
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)
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)
with open("./sampleOutput.txt","r") as f:
sampleResponse =f.read()
sampleResponse = sampleResponse.split('\n')
sampleResponse = [item.replace("'", "").replace('"', "") for item in sampleResponse]
sampleResponse