Feature: Multi-dataset support for PhysioNet (MIMIC-IV, eICU etc.)#64
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hannesill wants to merge 5 commits intorafiattrach:mainfrom
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Feature: Multi-dataset support for PhysioNet (MIMIC-IV, eICU etc.)#64hannesill wants to merge 5 commits intorafiattrach:mainfrom
hannesill wants to merge 5 commits intorafiattrach:mainfrom
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- Implement dynamic dataset registry supporting MIMIC-IV Demo and Full in src/m3/datasets.py - Add BigQuery backend support to MCP server for cloud-based data access in src/m3/mcp_server.py - Update CLI to handle credentialed dataset initialization and active dataset switching in src/m3/cli.py - Enhance MCP server tools to be dataset-aware and provide better error guidance - Improve security with SQL injection prevention and safer query execution - Refactor configuration management in src/m3/config.py for better runtime dataset detection - Update CI workflows for uv integration - Add comprehensive tests for new backends and tools
- Implement 'm3 use' CLI command to switch between active datasets (e.g., mimic-iv-full, eicu). - Update MCP server to dynamically resolve database paths and BigQuery configurations based on the active dataset. - Enhance 'm3 init' to handle datasets requiring authentication by providing 'wget' instructions for PhysioNet. - Update 'get_database_schema', 'get_table_info', and convenience tools to be dataset-aware. - Add 'tests/test_dynamic_switching.py' to verify dataset switching logic.
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Summary
Generalizes the M3 architecture to support multiple tabular PhysioNet datasets beyond just MIMIC-IV. This allows researchers to switch between different clinical databases (like eICU) seamlessly within the same session.
Key Changes
DatasetRegistryandDatasetDefinitionto handle dataset metadata.m3_data/datasets/(no code changes required for new data).m3 use <dataset>command to switch active datasets instantly without restarting the MCP client.Testing
eicu.jsonand running initialization.Notes
The tools get_icu_stays, get_lab_results, and get_race_distribution are MIMIC-IV specific. In the future, we could try to make them as generalizable as possible or register dataset-specific tools.
For now this is fine because the three core tools are dataset agnostic.