About 80% of patients’ health data is unstructured. Healthcare organizations are looking to extract insights from multi-model health data to better understand patient health profiles and drive health outcomes.
Snowflake’s Healthcare & Life Sciences Data Cloud supports multi-modal health data, enabling healthcare organizations to store, enrich and derive insights from sources such as clinical documents and DICOM files.
Join Snowflake product experts for a demonstration of a solution to detect pneumonia on chest x-rays leveraging Snowflake’s latest ML features and capabilities. The solution enables the user to load DICOM image data, train the model using Pytorch within Snowpark Container Services and easily deploy a model using Snowflake’s native model registry to detect probability of pneumonia.
Tune into this demo to see firsthand:
- A medical image processing solution architecture that runs on GPUs in Snowflake Snowpark Container Services
- How a Medical Imaging Metadata Store eases cohort discovery for research and population health analytics
- How to build and deploy predictive ML models to augment and speed up clinical decision-making with the Snowflake Healthcare & Life Sciences Data Cloud
Industry Field CTO, Healthcare
Data Cloud Architect - GSI