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Applied Machine Learning for Healthcare and Life Sciences using AWS

Name: Applied Machine Learning for Healthcare and Life Sciences using AWS
Author: Ujjwal Ratan
ISBN: 9781804610213

by Ujjwal Ratan

November 2022

Intermediate to advanced

224 pages

6h 48m

English

Packt Publishing

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Applied Machine Learning for Healthcare and Life Sciences Using AWS
ContributorsAbout the authorAbout the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesDownload the color imagesConventions usedGet in touchShare Your ThoughtsDownload a free PDF copy of this book
Part 1: Introduction to Machine Learning on AWS
Chapter 1: Introducing Machine Learning and the AWS Machine Learning Stack
What is ML?Supervised versus unsupervised learning ML terminologyExploring the ML life cycleProblem definitionData processing and feature engineeringModel training and deploymentIntroducing ML on AWSIntroducing the AWS ML stackSummary
Chapter 2: Exploring Key AWS Machine Learning Services for Healthcare and Life Sciences
Applying ML in healthcare and life sciencesHealthcare providersHealthcare payorsMedical devices and imagingPharmaceutical organizationsGenomics Introducing AWS AI/ML services for healthcare and life sciencesIntroducing Amazon Comprehend MedicalIntroducing Amazon Transcribe MedicalIntroducing Amazon HealthLakeOther AI services for healthcare and life sciencesSummaryFurther reading
Part 2: Machine Learning Applications in the Healthcare Industry
Chapter 3: Machine Learning for Patient Risk Stratification
Technical requirementsUnderstanding risk stratificationIdentifying at-risk patientsImplementing ML for patient risk stratificationIntroducing SageMaker CanvasImplementing ML for breast cancer risk predictionImporting your datasetBuilding the modelAnalyzing the modelPredicting from the modelSummary
Chapter 4: Using Machine Learning to Improve Operational Efficiency for Healthcare Providers
Technical requirementsIntroducing operational efficiency in healthcareAutomating clinical document processing in healthcareWorking with voice-based applications in healthcareBuilding a smart medical transcription application on AWSCreating an S3 bucketDownloading the audio file and Python scriptRunning the applicationSummaryFurther reading
Chapter 5: Implementing Machine Learning for Healthcare Payors
Technical requirementsIntroducing healthcare claims processingImplementing ML in healthcare claims processing workflowsIntroducing Amazon SageMaker StudioSageMaker Data WranglerSageMaker Studio notebooksBuilding an ML model to predict Medicare claim amountsAcquiring the dataFeature engineeringBuilding, training, and evaluating the modelSummary
Chapter 6: Implementing Machine Learning for Medical Devices and Radiology Images
Technical requirementsIntroducing medical devicesClasses of medical devicesUnderstanding SaMDIntroducing radiology imaging system componentsApplying ML to medical devices and radiology imagingIntroducing Amazon SageMaker trainingUnderstanding the SageMaker training architectureUnderstanding training options with SageMaker trainingBuilding a medical image classification model using SageMakerAcquiring the dataset and codeSummary

Part 3: Machine Learning Applications in the Life Sciences Industry
Chapter 7: Applying Machine Learning to Genomics
Technical requirementsIntroducing genomic sequencingCategorizing genomic sequencing stagesLooking into the evolution of genomic sequencingChallenges with processing genomic dataStorage volumeSharing, access control, and privacyComputeInterpretation and analysisApplying ML to genomic workflowsIntroducing Amazon SageMaker InferenceUnderstanding real-time endpoint options on SageMakerUnderstanding Serverless Inference on SageMakerUnderstanding Asynchronous Inference on SageMakerUnderstanding batch transform on SageMakerBuilding a genomic and clinical NER applicationAcquiring the genomic test reportUnderstanding the pre-trained genetic entity detection modelSummaryFurther reading
Chapter 8: Applying Machine Learning to Molecular Data
Technical requirementsUnderstanding molecular dataSmall moleculesLarge moleculesIntroducing drug discovery and designUnderstanding structure-based drug designApplying ML to molecular dataMolecular reaction predictionMolecular property predictionMolecular structure predictionLanguage models for proteins and compoundsIntroducing custom containers in SageMakerAdapting your container for SageMaker trainingAdapting your container for SageMaker inferenceBuilding a molecular property prediction model on SageMakerRunning the Jupyter NotebookSummaryFurther reading
Chapter 9: Applying Machine Learning to Clinical Trials and Pharmacovigilance
Technical requirementsUnderstanding the clinical trial workflowUnderstanding RWE studiesIntroducing PVSignal generationHypothesis testingApplying ML to clinical trials and PVLiterature search and protocol designTrial participant recruitmentAdverse event detection and reportingReal-world data analysisIntroducing SageMaker Pipelines and Model RegistryDefining pipeline and stepsCaching pipelinesIntroducing the SageMaker Model RegistryBuilding an adverse event clustering model pipeline on SageMakerRunning the Jupyter notebooksReviewing the pipeline and modelDeploying model and running inferenceSummary
Chapter 10: Utilizing Machine Learning in the Pharmaceutical Supply Chain
Technical requirementsUnderstanding the pharmaceutical supply chain landscapeDrug manufacturersDistributors or wholesalersConsumersSummarizing key challenges faced by the pharmaceutical supply chain industryIntroducing pharmaceutical salesApplying ML to the pharmaceutical supply chain and salesTargeting providers and patientsForecasting drug demand and sales outlookImplementing predictive maintenanceUnderstanding market sentiment and completive analysisIntroducing Amazon ForecastAmazon Forecast algorithmsImporting a datasetTraining forecasting modelsGenerating forecastsBuilding a pharmaceutical sales forecasting model using Amazon ForecastAcquiring the datasetRunning the Jupyter notebookSummary
Part 4: Challenges and the Future of AI in Healthcare and Life Sciences
Chapter 11: Understanding Common Industry Challenges and Solutions
Technical requirementsUnderstanding challenges with implementing ML in healthcare and life sciencesHealthcare and life sciences regulationsSecurity and privacyBias and transparency in MLReproducibility and generalizability of ML modelsUnderstanding options for solving these challengesEncrypting and anonymizing your dataExplainability and bias detectionBuilding reproducible ML pipelinesAuditability and reviewIntroducing SageMaker Clarify and Model MonitorDetecting bias using SageMaker ClarifyPre-training bias metricsPost-training bias metricsExplaining model predictions with SageMaker ClarifyMonitoring models with SageMaker Model MonitorDetecting bias and explaining model predictions for healthcare coverage amountsAcquiring the datasetRunning the Jupyter notebooksViewing bias and explainability reports in SageMaker StudioSummary
Chapter 12: Understanding Current Industry Trends and Future Applications
Key factors influencing advancements of AI in healthcare and life sciencesAvailability of multimodal dataActive learning with human-in-the-loop pipelinesDemocratization with no-code AI toolsBetter-performing infrastructure and models Better focus on responsible AIUnderstanding current industry trends in the application of AI for healthcare and life sciencesCuring incurable diseases such as cancerTelehealth and remote careUsing the Internet of Things (IoT) and roboticsSimulations using digital twinsSurveying the future of AI in healthcareReinforcement learningFederated learningVirtual realityBlockchainQuantum computingConcluding thoughtsSummary
Index
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Content preview from Applied Machine Learning for Healthcare and Life Sciences using AWS

5 Implementing Machine Learning for Healthcare Payors

Health insurance is an integral part of a person’s well-being and financial security. Unlike countries such as the UK and Canada, the US does not have a concept of universal healthcare. The majority of US residents are covered by plans from their employers who contract with private health insurance companies. Others rely on public insurance such as Medicare and Medicaid. The rising cost of healthcare has made it almost impossible for anyone to survive without health insurance. No wonder that, by the end of 2020, over 297 million people in the US had coverage for health insurance, with the number trending higher every year.

Health insurance companies, also known as healthcare payors, are organizations ...

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ISBN: 9781804610213

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Applied Machine Learning for Healthcare and Life Sciences using AWS

by Ujjwal Ratan

5

Implementing Machine Learning for Healthcare Payors

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.