The AIR·MS data platform for artificial intelligence in healthcare
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View abstract on PubMed
Summary
This summary is machine-generated.The Artificial Intelligence-Ready Mount Sinai (AIR·MS) platform unifies clinical data and computational resources, enabling large-scale AI research. It demonstrates high performance for cohort building and AI model training across diverse healthcare datasets.
Area Of Science
- Clinical Informatics
- Artificial Intelligence in Healthcare
- Data Science
Background
- Healthcare systems generate vast amounts of diverse clinical data.
- Integrating disparate data sources and computational infrastructure is crucial for advanced AI research.
- Existing platforms often lack the scale and flexibility required for complex AI model development.
Purpose Of The Study
- To introduce the Artificial Intelligence-Ready Mount Sinai (AIR·MS) platform.
- To showcase the integration of diverse clinical datasets and computational infrastructure.
- To demonstrate the platform's utility through three AI research projects.
Main Methods
- AIR·MS integrates structured EHR data and unstructured pathology/radiology data using the OMOP Common Data Model.
- Data is stored in an in-memory columnar database, with metadata linking raw source data.
- HIPAA-compliant cloud and on-premises HPC environments support data access and analytics.
Main Results
- The platform provides access to over 12 million patient records, including EHRs, clinical notes, and imaging metadata.
- AIR·MS supports interactive cohort building and AI model training with high system performance.
- Three use cases demonstrated risk-factor discovery and federated cardiovascular risk modeling.
Conclusions
- AIR·MS successfully integrates clinical data and infrastructure for large-scale AI research.
- The platform's scalability, security, and collaborative design serve as a model for future initiatives.
- AIR·MS enables AI-driven healthcare research on multimodal clinical data.
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