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Healthcare digitization has led to an explosion of data. Payers, providers and pharmaceutical organizations alike are producing petabytes of data ranging from electronic health records (EHR) to medical images to DNA sequence data and beyond. The challenge today is how to prepare these large, diverse datasets for analytics and machine learning (ML) at scale in order to unlock novel patient insights. Unfortunately, legacy technology investments have created an environment where data is locked in silos making it hard to federate data and scale analytics. Many organizations have tried to mitigate this by replicating data across data warehouses, but this results in higher costs and data governance issues. These challenges are compounded by the lack of support for advanced analytics and ML. The solution is a modern clinical data lake in the cloud.
In this virtual workshop, we’ll share how a unified approach to data analytics can accelerate analytics and ML projects to deliver on a wide range of use cases in the Healthcare and Life Sciences industry. More, specifically you’ll learn how to:
– Build a scalable clinical data lake with powerful open-source technologies like Delta Lake and Apache SparkTM
– Ingest and prepare streaming EHR data for downstream analytics
– Build a patient cohort browser and use ML to predict disease risk and care utilization
– Use MLflow to collaboratively build and track ML experiments in a reproducible and HIPAA-compliant environment