使用 Amazon Textract 和 Amazon Comprehend Medical 实现无服务器化的医疗文档分析（一）

{  "Version": "2012-10-17",  "Statement": [    {      "Effect": "Allow",      "Action": [        "textract:*",        "s3:*",        "cloudwatch:*",        "logs:*",        "iam:GetPolicy",        "iam:GetPolicyVersion",        "iam:GetRole"      ],      "Resource": "*"    },    {      "Effect": "Allow",      "Action": "iam:CreateServiceLinkedRole",      "Resource": "arn:aws:iam::*:role/aws-service-role/events.amazonaws.com/AWSServiceRoleForCloudWatchEvents*",      "Condition": {        "StringLike": {          "iam:AWSServiceName": "events.amazonaws.com"        }      }    }  ]}

{  "Version": "2012-10-17",  "Statement": [    {      "Effect": "Allow",      "Action": [        "comprehendmedical:*",        "s3:*",        "cloudwatch:*",        "logs:*",        "iam:GetPolicy",        "iam:GetPolicyVersion",        "iam:GetRole"      ],      "Resource": "*"    },    {      "Effect": "Allow",      "Action": "iam:CreateServiceLinkedRole",      "Resource": "arn:aws:iam::*:role/aws-service-role/events.amazonaws.com/AWSServiceRoleForCloudWatchEvents*",      "Condition": {        "StringLike": {          "iam:AWSServiceName": "events.amazonaws.com"        }      }    }  ]}

import boto3import json
def lambda_handler(event, context):    # File definition    s3Key = event['Records'][0]['s3']['object']['key']    keyName = s3Key.split('/')[1].split('.')[0]    outFile = '/tmp/output.json'    outputKey = 'raw/' + keyName + '/raw.json'    medicalRaw = "/tmp/medicalraw.txt"    medicalRawKey = 'raw/' + keyName + '/medicalraw.txt'    medicalReport = '/tmp/medicalreport.txt'    medicalReportKey = 'medical/' + keyName + '/medicalreport.txt'
    # S3 and Textract Configuration    s3Bucket = event['Records'][0]['s3']['bucket']['name']    fileType = 'FORMS'
    # Call Textract to convert form to json    textract = boto3.client('textract')    textractResponse = textract.analyze_document(        Document={            'S3Object': {                'Bucket': s3Bucket,                'Name': s3Key            }        },        FeatureTypes=[            fileType        ]    )    with open(outFile, 'w') as outfile:        outfile.write(json.dumps(textractResponse, indent=4))
    # Ingest content    for blocks in textractResponse['Blocks'][1:]:        if blocks['Confidence']:            if (blocks['Confidence'] >= 70) and (blocks['BlockType'] == 'LINE'):                with open(medicalReport, 'a') as medicalReportOut:                    medicalReportOut.write(blocks['Text'] + "\r\n")            elif (blocks['Confidence'] >= 70) and (blocks['BlockType'] == 'WORD'):                with open(medicalRaw, 'a') as medicalRawOut:                    medicalRawOut.write(blocks['Text'] + "\r\n")            else:                continue        else:            print("oops")
    # Upload outputs to s3    s3 = boto3.resource('s3')    try:        s3.meta.client.upload_file(outFile, s3Bucket, outputKey)        s3.meta.client.upload_file(medicalReport, s3Bucket, medicalReportKey)        s3.meta.client.upload_file(medicalRaw, s3Bucket, medicalRawKey)    except Exception as e:        print(e)        print("Upload failed!")    else:        print("Upload done!")

import boto3import json
def lambda_handler(event, context):    # Configure definition    s3Bucket = event['Records'][0]['s3']['bucket']['name']    s3Key = event['Records'][0]['s3']['object']['key']    keyName = s3Key.split('/')[1].split('.')[0]    localFinal = '/tmp/result.txt'    phiFinal = '/tmp/phi.txt'    manualFinal = '/tmp/manual.txt'    medicalResult = 'result/' + keyName + '/medicalresult.txt'    phiResult = 'phi/' + keyName + '/phi.txt'    manualResult = 'manual/' + keyName + '/manual.txt'
    # Ingest medical report    try:        s3 = boto3.client('s3')    except Exception as e:        print(e)        print('connect S3 failed!')    else:        print('connect S3 successfully')
    s3_object = s3.get_object(Bucket=s3Bucket, Key=s3Key)    body = s3_object['Body']
    # Execute medical analysis    try:        comprehendMedical = boto3.client('comprehendmedical')    except Exception as e:        print(e)    else:        print('connect Comprehend Medical successfully')
    detectEntities = comprehendMedical.detect_entities_v2(        Text=body.read().decode('utf-8')    )    detectOutputRaw = detectEntities['Entities']
    # Categorize different types of information    report_ANATOMY = []    report_MEDICAL_CONDITION = []    report_MEDICATION = []    report_PROTECTED_HEALTH_INFORMATION = []    report_TEST_TREATMENT_PROCEDURE = []    report_MANUAL = []
    for ctgy in detectOutputRaw:        if ctgy['Score'] >= 0.6:            if ctgy['Category'] == 'ANATOMY':                report_ANATOMY.append(ctgy)            elif ctgy['Category'] == 'MEDICAL_CONDITION':                report_MEDICAL_CONDITION.append(ctgy)            elif ctgy['Category'] == 'MEDICATION':                report_MEDICATION.append(ctgy)            elif ctgy['Category'] == 'PROTECTED_HEALTH_INFORMATION':                report_PROTECTED_HEALTH_INFORMATION.append(ctgy)            elif ctgy['Category'] == 'TEST_TREATMENT_PROCEDURE':                report_TEST_TREATMENT_PROCEDURE.append(ctgy)            else:                continue        else:            report_MANUAL.append(ctgy)
    result_ANATOMY = []    result_MEDICAL_CONDITION = []    result_MEDICATION = []    result_PROTECTED_HEALTH_INFORMATION = []    result_TEST_TREATMENT_PROCEDURE = []    result_MANUAL = []
    if report_ANATOMY:        for anatomy in report_ANATOMY:            result_ANATOMY.append(anatomy['Text'])    if report_MEDICAL_CONDITION:        for medical_condition in report_ANATOMY:            result_MEDICAL_CONDITION.append(medical_condition['Text'])    if report_MEDICATION:        for medication in report_MEDICATION:            result_MEDICATION.append(medication['Text'])    if report_PROTECTED_HEALTH_INFORMATION:        for protected_health_information in report_PROTECTED_HEALTH_INFORMATION:            result_PROTECTED_HEALTH_INFORMATION.append(protected_health_information['Text'])    if report_TEST_TREATMENT_PROCEDURE:        for test_treatment_procedure in report_TEST_TREATMENT_PROCEDURE:            result_TEST_TREATMENT_PROCEDURE.append(test_treatment_procedure['Text'])    if report_MANUAL:        for test_manual in report_MANUAL:            result_MANUAL.append(test_manual['Text'])
    with open(localFinal, 'a') as localfile:        if result_ANATOMY:            localfile.write('Anatomy:\r\n' + '\r\n'.join(set(result_ANATOMY)))        if result_MEDICAL_CONDITION:            localfile.write('\r\n---\r\n')            localfile.write('Medical Condition:\r\n' + '\r\n'.join(set(result_MEDICAL_CONDITION)))        if result_MEDICATION:            localfile.write('\r\n---\r\n')            localfile.write('Medication:\r\n' + '\r\n'.join(set(result_MEDICATION)))        if result_TEST_TREATMENT_PROCEDURE:            localfile.write('\r\n---\r\n')            localfile.write('Test Treatment Procedure:\r\n' + '\r\n'.join(set(result_TEST_TREATMENT_PROCEDURE)))    localfile.close()
    with open(phiFinal, 'a') as phifile:        if result_PROTECTED_HEALTH_INFORMATION:            phifile.write('Protected Health Information:\r\n' + '\r\n'.join(set(result_PROTECTED_HEALTH_INFORMATION)))    phifile.close()
    with open(manualFinal, 'a') as manualfile:        if result_MANUAL:            manualfile.write('Manually Check:\r\n' + '\r\n'.join(set(result_MANUAL)))    manualfile.close()
    # Upload outputs to s3    s3Upload = boto3.resource('s3')    try:        s3Upload.meta.client.upload_file(localFinal, s3Bucket, medicalResult)        s3Upload.meta.client.upload_file(phiFinal, s3Bucket, phiResult)        s3Upload.meta.client.upload_file(manualFinal, s3Bucket, manualResult)
    except Exception as e:        print(e)        print("Upload failed!")    else:        print("Upload done!")