meds-biobank 0.0.1

Unofficial, lightweight Python re-implementation of parts of the **MEDS** software ecosystem, built to operate on in-memory tables loaded via PySpark rather than directly on disk. Designed for small-to-medium biobanks queried through cloud services in interactive Python notebooks.

meds-biobank

Unofficial, lightweight Python re-implementation of parts of the MEDS software ecosystem, built to operate on in-memory tables loaded via PySpark rather than directly on disk. Designed for small-to-medium biobanks queried through cloud services in interactive Python notebooks.

Primary target: Penn Medicine Biobank

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Table of Contents

• Features
  • ETL
    • OMOP MEDS-ETL 0.1.3 (Nested)
    • OMOP MEDS-ETL 0.3.11 (Flat)
  • Ontology Transforms

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Features

ETL

OMOP MEDS-ETL 0.1.3

Path	/meds-biobank/src/meds-biobank/etl_pipelines/omop_meds_nwsted.py
Description	Re-implementation of src/meds_etl/omop.py from meds_etl v0.1.3. Converts OMOP v5.4/5.3 → MEDS v0.1.3 (nested format). Use with CLMBR-T-base / FEMR v0.2.3.

Workflow

1. Extract all events
2. Prune / deduplicate patient event streams
3. Convert event streams into nested patient representations

Differences from source Pruning (delta_encode, remove_nones) happens before finalizing the MEDS mapping.

Supported Tables person · visit_occurrence · procedure_occurrence · condition_occurrence · drug_exposure · observation · measurements · death

Options

• Pre-ETL of measurements: value/unit conversion + separation into labs and vitals
  • Sub-options: where possible vs. drop messy

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OMOP MEDS-ETL 0.3.11

Path	/meds-biobank/src/meds-biobank/etl_pipelines/omop_nested_flat.py
Description	Re-implementation of src/meds_etl/omop.py from meds_etl v0.3.11. Converts OMOP v5.4/5.3 → MEDS v0.3.3 (flat format, handles visit discharge).

Workflow

1. Extract all events
2. Prune / deduplicate patient event streams
3. Order event streams by patient, time

Differences from source Pruning (delta_encode, remove_nones) happens within the ETL, rather than as part of the tokenizer (FEMR 0.2.3 transforms sub-module).

Supported Tables person · visit_occurrence · procedure_occurrence · condition_occurrence · drug_exposure · observation · measurements · death

Options

• Pre-ETL of measurements: value/unit conversion + separation into labs and vitals
  • Sub-options: where possible vs. drop messy

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Ontology Transforms

Code Vocabulary

• vocab_id / concept_code
• Original concept_id
• Categorizations (CCS, Phecodes)

Measurement Representation

• Decile-binned with zero-handling