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MEDS Ecosystem

Tools, dataset ETLs, and models that work with MEDS. Search by name or description, or filter by topic below. Not sure where to start? Which tool do I use for what? walks through the MEDS workflow step by step. Dataset statistics compares the datasets that the public ETLs produce.

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Know of a tool, model, or dataset ETL that works with MEDS but isn't listed here? Request that it be added using our issue template. Pull requests that add it to static/data/ecosystem.yaml are also welcome!

Tools
Tools/MEDS Core
Tools/Preprocessing
Tools/Exploration and Visualization
Tools/Task Extraction
Tools/Modeling
Tools/Model Evaluation
Tools/Benchmarking
Tools/Testing
Tools/Semantic Web
Dataset ETLs
Dataset ETLs/ETL Templates/Tools
Dataset ETLs/Public Datasets
Dataset ETLs/Alternate Data Standards
Published Models
MEDS Schema

Defines the MEDS schemas for events, dataset and code metadata, subject splits and labels, with matching Python types.

⭐ 143 stars

📦 0.4.1

GitHubPyPI
MEDS-Reader

Converts a MEDS dataset into a fast database and reads its subjects as Python objects, to process with plain Python.

⭐ 22 stars

📦 0.2.0

MEDS Transforms

Builds and runs pre-processing pipelines over MEDS datasets from reusable stages, such as filtering, normalization and outlier detection.

⭐ 41 stars

📦 0.7.1

MEDS TorchData

Pre-processes a MEDS dataset into tensors and loads it as a PyTorch dataset for training models.

⭐ 5 stars

📦 0.9.0

MEDS-Inspect

An interactive web app for visually exploring the codes and contents of a MEDS dataset, for example to check an ETL.

⭐ 7 stars

📦 0.0.11

suMEDS

Summarizes a MEDS dataset into a compact code catalog with descriptions, related codes and counts, masking or removing rare codes.

⭐ 3 stars

📦 0.6.3

MEDS Summary Stats

Computes privacy-safe summary statistics of MEDS datasets and compares them, to validate ETLs and catch regressions between versions.

⭐ 2 stars

📦 0.0.4

MEDS Visualizations

Plots data extracted from MEDS datasets, such as code frequencies, in Jupyter notebooks.

⭐ 1 stars

📦 0.0.5

ACES

Extracts task-specific cohorts from MEDS datasets, using a configuration file that defines the task's predicates and criteria.

⭐ 50 stars

📦 0.7.4

MEDS Random Task Sampler

Generates reproducible, model-independent task rows from MEDS datasets by pairing codes and time horizons with sampled patient prediction times.

⭐ 0 stars

📦 v0.2.0

ehrlint

Audits a prediction task on a MEDS or OMOP dataset for leakage, such as post-index features or patient overlap across splits.

⭐ 0 stars

MEDS-Torch

Trains deep learning models on MEDS data, either supervised or pretrained and fine-tuned, with several ways to tokenize events.

⭐ 30 stars

📦 0.0.8

FEMR

Supports machine learning on MEDS data with ontology tools, labeling, tabular featurization and training of MOTOR foundation models.

⭐ 185 stars

📦 0.2.4

MEDS to CEHR

Converts MEDS datasets into CEHR-GPT's visit-based patient sequences, without depending on the CEHR-GPT model itself.

⭐ 0 stars

meds2text

Transforms MEDS data extracted from OMOP CDM sources into textual representations of patient records.

⭐ 3 stars

TwinWeaver

Turns patient histories, including MEDS data, into text prompts so that LLMs can forecast biomarkers and predict clinical events.

⭐ 24 stars

meds-evaluation

Evaluates the predictions of models built in the MEDS ecosystem on binary classification tasks, and writes the results to JSON.

⭐ 8 stars

📦 0.0.6

MEDS-DEV

A decentralized benchmark in which data owners run shared ACES tasks and model training recipes on their own MEDS datasets.

⭐ 44 stars

📦 0.2.0

MEDS Testing Helpers

Provides pytest fixtures, sample MEDS datasets and generated larger datasets for testing and benchmarking MEDS tools and pipelines.

⭐ 4 stars

📦 0.3.1

MEDS Ontology

An OWL ontology and SHACL shapes that formally encode the MEDS schemas, from subjects and measurements to splits and labels.

⭐ 1 stars

📦 v1.0.2

MEDS2RDF

Converts MEDS datasets into RDF knowledge graphs based on the MEDS Ontology, streaming the triples to memory or to files.

⭐ 4 stars

📦 v2.0.0

ETL Template

A Copier template for creating a config-only MEDS-Extract ETL package that converts a new dataset into MEDS.

⭐ 7 stars

📦 0.1.0

MEDS-ETL

A collection of ETLs that convert MIMIC-IV 2.2 and OMOP CDM 5.3 and 5.4 data into MEDS 0.3.3.

_Use for the MIMIC dataset is deprecated_!

⭐ 44 stars

📦 0.3.11

MEDS Extract

Converts raw EHR data into MEDS using ETL pipelines that are declared in a configuration file and built on MEDS-Transforms.

⭐ 9 stars

📦 0.8.1

EHR2Trace

Converts hospital EHR exports into OMOP CDM 5.4 and MEDS 0.4, linking every output row to its source rows.

⭐ 4 stars

📦 v0.7.0

WeavEHR

Extracts and harmonizes ICU data from sources such as MIMIC-IV, eICU and OMOP exports into MEDS, using declarative YAML configurations.

⭐ 2 stars

📦 0.1.2

EHR2MEDS

Formats raw EHR data dumps and converts them to MEDS, via an intermediate format called preMEDS.

⭐ 4 stars

MESSY Synth

Generates synthetic raw data matching a MEDS-Extract config, so an ETL can be demoed or smoke-tested without the real dataset.

⭐ 0 stars

📦 0.0.1

MIMIC-IV

Converts MIMIC-IV, a dataset of emergency department and ICU patients at Beth Israel Deaconess Medical Center in Boston, into MEDS.

🚀 Demo Available

⭐ 27 stars

📦 0.2.0

MIMIC-IV Demo

The MIMIC-IV Clinical Database Demo, with 100 critical care patients, already converted into MEDS and openly downloadable.

AUMCdb

Converts AmsterdamUMCdb, a de-identified ICU database from Amsterdam University Medical Centers, into MEDS.

⭐ 3 stars

📦 0.1.0

NWICU

Converts NWICU, the Northwestern ICU database from a network of hospitals in Chicago, into MEDS.

⭐ 4 stars

📦 0.0.13

SICdb

Converts SICdb, the Salzburg Intensive Care database of admissions to four ICUs at University Hospital Salzburg, into MEDS.

⭐ 6 stars

📦 0.1.0

INSPIRE

Converts INSPIRE, a perioperative dataset of about 130,000 surgical cases from an academic institution in South Korea, into MEDS.

⭐ 3 stars

📦 v0.1.0

eICU

Converts the eICU Collaborative Research Database, a multi-center database of ICU admissions across the United States, into MEDS.

🚀 Demo Available

⭐ 7 stars

📦 0.2.0

HiRID

Converts HiRID, a high time-resolution ICU dataset from Bern University Hospital in Switzerland, into MEDS.

⭐ 3 stars

📦 0.1.0

CDSL

Converts the tabular data of CDSL, on patients hospitalized with confirmed or suspected COVID-19 at Spain's HM Hospitales, into MEDS.

⭐ 0 stars

📦 0.0.1

ALOTT

Converts the EMR tables of ALOTT, a database pairing bedside telemetry with EMR data from two hospitals, into MEDS.

⭐ 0 stars

📦 0.0.1

PIC

Converts the Paediatric Intensive Care (PIC) database, from the Children's Hospital of Zhejiang University School of Medicine, into MEDS.

⭐ 0 stars

📦 0.0.3

Zigong

Converts a database of ICU patients with infection at Zigong Fourth People's Hospital in Sichuan, China, into MEDS.

⭐ 0 stars

📦 0.3.1

MC-MED

Converts MC-MED, clinical and monitoring data from adult emergency department visits at Stanford Health Care, into MEDS.

⭐ 0 stars

📦 0.1.0

K-MIMIC (synthetic)

Converts Synthetic K-MIMIC (SYN-ICU), synthetic ICU data generated from Seoul National University Hospital records, into MEDS.

⭐ 0 stars

EHRSHOT

A benchmark for few-shot evaluation of EHR foundation models, with the full records of 6,739 Stanford Medicine patients and 15 tasks.

_Distributed natively in MEDS (`EHRSHOT_MEDS.zip` on Redivis); no ETL needed._

⭐ 234 stars

OMOP (via MEDS-ETL)

Converts OMOP CDM 5.3 and 5.4 datasets into MEDS 0.3.3 with the OMOP ETL of MEDS-ETL.

⭐ 44 stars

📦 0.3.11

OMOP (via dedicated OMOP_MEDS)

Converts OMOP CDM 5.3, 5.4 and 5.5 datasets into MEDS with a pipeline built on MEDS-Extract.

⭐ 5 stars

📦 v0.4.0

FHIR

Converts HL7 FHIR R4 and R5 resources, from bulk-export NDJSON files or patient bundles, into MEDS.

⭐ 3 stars

📦 0.2.0

CEHR-BERT

Pretrains a BERT-style model on MEDS or OMOP data, using temporal information, then fine-tunes it for clinical prediction tasks.

⭐ 50 stars

📦 v1.5.0

MEDS-Tab

Tabularizes MEDS data into sparse features aggregated over time windows and trains baseline models, mainly XGBoost, on them.

⭐ 21 stars

📦 0.3.0

GenHPF

A general healthcare prediction model that embeds EHR data from any system, including MEDS, without code mapping or feature selection.

⭐ 25 stars

📦 v1.0.12

EHR-RAGp

A retrieval-augmented clinical prediction model that retrieves relevant segments of a patient's own EHR history, using MEDS-based preprocessing.

⭐ 2 stars

CEHR-GPT

A GPT-style foundation model for structured EHR data, including MEDS, for patient representations, zero-shot prediction and synthetic data generation.

⭐ 20 stars

📦 v0.1.10

MOTOR

A time-to-event foundation model, pretrained on Stanford Medicine EHRs, that produces patient representations for predicting when clinical events occur.

_Reads MEDS v0.3; pretrained weights are gated (CC-BY-NC 4.0)._

⭐ 185 stars

📦 0.2.4

CLMBR-T-base

A 141-million-parameter autoregressive foundation model, pretrained on Stanford Medicine EHRs, that turns MEDS patient records into representations for prediction.

_Pretrained weights are gated (CC-BY-NC 4.0)._

⭐ 234 stars

Context Clues

Pretrained long-context EHR foundation models (GPT, Llama, Mamba and Hyena), with code for training Hugging Face models on MEDS data.

_Pretrained weights are gated (CC-BY-NC 4.0)._

⭐ 55 stars

📦 v0.1.2

ETHOS

A GPT-2-based EHR foundation model that tokenizes MEDS data and makes zero-shot predictions by generating future patient health trajectories.

⭐ 68 stars

MEDS-EIC-AR

An "everything-is-code" autoregressive generative model for MEDS data, capable of zero-shot inference.

⭐ 12 stars

📦 0.5.1

EveryQuery

A promptable foundation model trained on MEDS data that answers whether a code will occur in a future time window.

⭐ 2 stars

📦 v0.7.0

BONSAI

Turns MEDS records into tokenized patient histories, pretrains a GPT- or BERT-style transformer on them and fine-tunes it for outcome prediction.

⭐ 14 stars

📦 v2.0.1