Amazon SageMaker for Healthcare and Life Sciences

To make decisions faster, healthcare and life sciences organizations need to understand text in medical documents, such as patient forms. With Amazon SageMaker, you can build ML models to automatically extract, process, and analyze data from handwritten and electronic documents so you can process documents faster and more accurately. SageMaker provides built-in ML algorithms that are optimized for text classification, natural language processing (NLP), and optical character recognition (OCR), that you can readily use to train and deploy models, or you can use Amazon SageMaker Autopilot to automatically generate text processing models.

To keep patient data secure, it is important for healthcare and life sciences organizations to use fraud detection models to spot suspicious healthcare claims before they impact customers. With Amazon SageMaker, you can build ML models to detect suspicious transactions before they occur and alert your customers in a timely fashion. SageMaker provides built-in ML algorithms, such as Random Cut Forrest and XGBoost, that you can use to train and deploy fraud detection models. In addition, SageMaker provides a set of solutions for fraud detection that can be deployed with just a few clicks. 

Healthcare and life sciences organizations continue to look for ways to automatically identify anomalies and accelerate patient diagnosis. With Amazon SageMaker, you can build computer vision models to spot anomalies in medical images and automatically flag for deep analysis and diagnosis. SageMaker provides a broad set of capabilities purpose-built for machine learning including built-in algorithms that are optimized for computer vision, such as image classification, that can improve the diagnosis of patients, reduce the subjectivity in diagnosis, and help save time for pathologists.