---
language:
- en
license: mit
size_categories:
- n<1K
task_categories:
- time-series-forecasting
- tabular-classification
- feature-extraction
task_ids:
- tabular-multi-class-classification
pretty_name: CGM-JEPA Downstream Evaluation Splits
modalities:
- Time Series
- Tabular
tags:
- continuous-glucose-monitor
- cgm
- insulin-resistance
- beta-cell-dysfunction
- metabolic-subphenotype
- ogtt
- healthcare
- time-series
- subject-level-classification
configs:
- config_name: default
  data_files:
  - split: train
    path: train.parquet
  - split: validation
    path: validation.parquet
---

# CGM-JEPA Downstream Evaluation Splits

[Paper](https://huggingface.co/papers/2605.00933) | [Code](https://github.com/cruiseresearchgroup/CGM-JEPA)

Labeled cohort splits used to evaluate CGM encoders on two binary metabolic outcomes — **insulin resistance** and **β-cell dysfunction** — in the paper [CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining](https://huggingface.co/papers/2605.00933).

> Downstream-only. For the unlabeled pretraining corpus (Stanford + Colas), see [`CRUISEResearchGroup/CGM-JEPA-Pretraining`](https://huggingface.co/datasets/CRUISEResearchGroup/CGM-JEPA-Pretraining). For pretrained encoder weights, see [`CRUISEResearchGroup/CGM-JEPA`](https://huggingface.co/CRUISEResearchGroup/CGM-JEPA).

## Quick start

### Option 1 — `datasets` library (recommended for analysis / fine-tuning)

```python
from datasets import load_dataset
ds = load_dataset("CRUISEResearchGroup/CGM-JEPA-Downstream")
# DatasetDict({
#   train:      Dataset({features: ['subject', 'ctru_venous', 'ctru_cgm', ...,
#                                   'ir_class', 'ir_regression',
#                                   'beta_class', 'beta_regression'],
#                       num_rows: 27}),
#   validation: Dataset({..., num_rows: 17})
# })
```

The two splits share a canonical 11-column schema (subject + 6 modality `Sequence(Value('float64'))` + 4 label fields). Modalities the train cohort doesn't have are `None` rather than empty — only `ctru_venous` is populated in the train split.

### Option 2 — original nested JSON (used by the code repo's eval pipeline)

```bash
huggingface-cli download CRUISEResearchGroup/CGM-JEPA-Downstream \
  --repo-type dataset --local-dir Dataset_Open
```

Then from the [code repository](https://github.com/cruiseresearchgroup/CGM-JEPA):

```bash
# Reproduce all 3 evaluation regimes × 2 endpoints (Tables 1–6)
python scripts/run_all_eval.py
```

## Files

| File | Subjects | Size | Role |
|---|---|---|---|
| `train.parquet` | 27 | ~30 KB | **Initial cohort** in `datasets`-friendly tabular form (one row per subject). |
| `validation.parquet` | 17 | ~110 KB | **Validation cohort** in `datasets`-friendly tabular form. |
| `train_split.json` | 27 | ~45 KB | Same data as `train.parquet`, in the nested JSON layout the code repo's `data_loaders/` expects. |
| `validation_split.json` | 17 | ~146 KB | Same data as `validation.parquet`, nested JSON layout. |

The two cohorts are **subject-disjoint by construction**: subjects appearing in both upstream groups were removed from the validation cohort during preprocessing.

## Schema

Both files use the same nested-JSON structure:

```jsonc
{
  "S01": {                                  // subject identifier
    "x": {
      "ctru_venous":  [<float>, …],         // sequence of glucose values (mg/dL)
      "ctru_cgm":     [<float>, …],         //   (validation cohort only)
      "home_cgm_1":   [<float>, …],         //   "
      "home_cgm_2":   [<float>, …],         //   "
      "cgm_home_mean":[<float>, …],         //   mean of home_cgm_1 & home_cgm_2
      "cgm_all_mean": [<float>, …]          //   mean of ctru_cgm, home_cgm_1, home_cgm_2
    },
    "y": {
      "ir":   {"class": 0|1, "regression": <float>},   // SSPG-derived
      "beta": {"class": 0|1, "regression": <float>}    // DI-derived
    }
  },
  "S02": { ... },
  ...
}
```

### Extract methods (`x` sub-keys)

| Key | Availability | Description |
|---|---|---|
| `ctru_venous` | train + validation | In-clinic venous OGTT glucose trajectory |
| `ctru_cgm` | validation only | In-clinic CGM trajectory recorded during the same OGTT |
| `home_cgm_1` | validation only | First free-living home-CGM window |
| `home_cgm_2` | validation only | Second free-living home-CGM window |
| `cgm_home_mean` | validation only | Subject-level mean of `home_cgm_1` and `home_cgm_2` |
| `cgm_all_mean` | validation only | Subject-level mean of all three CGM modalities |

The initial cohort was defined to have OGTT venous data only (no matching CGM), so `train_split.json` contains a single `ctru_venous` field per subject.

### Labels (`y` sub-keys)

| Field | Type | Source | Threshold |
|---|---|---|---|
| `ir.class` | binary {0, 1} | SSPG (Steady-State Plasma Glucose) | 1 = insulin-resistant, 0 = insulin-sensitive |
| `ir.regression` | float | SSPG numeric value | mg/dL |
| `beta.class` | binary {0, 1} | DI (Disposition Index) | 1 = β-cell dysfunction, 0 = normal β-cell function |
| `beta.regression` | float | DI numeric value | dimensionless |

A class value of `-1` indicates a missing or unannotated label. Threshold definitions follow Metwally et al. (2025).

### Class distribution

| Cohort | n | IR=1 (resistant) | IR=0 (sensitive) | β=1 (dysfunction) | β=0 (normal) |
|---|---:|---:|---:|---:|---:|
| Initial (train_split) | 27 | 14 | 13 | 16 | 11 |
| Validation (validation_split) | 17 | 7 | 10 | 6 | 11 |

Both labels are reasonably balanced; the paper reports stratified 2-fold cross-validation over 20 random iterations (40 paired evaluations per cell).

## Evaluation regimes (paper Tables 1–6)

The two splits support all three deployment regimes evaluated in the paper:

| Regime | Train on | Test on |
|---|---|---|
| Cohort generalization (venous) | `train_split` × `ctru_venous` | `validation_split` × `ctru_venous` |
| Venous → home-CGM transfer | `validation_split` × `ctru_venous` | `validation_split` × `cgm_home_mean` |
| In-domain home CGM | `validation_split` × `cgm_home_mean` | `validation_split` × `cgm_home_mean` |

All regimes are orchestrated by [`scripts/run_all_eval.py`](https://github.com/cruiseresearchgroup/CGM-JEPA/blob/main/scripts/run_all_eval.py).

## How this corpus was built

The splits were assembled by [`scripts/preprocess_dataset.py`](https://github.com/cruiseresearchgroup/CGM-JEPA/blob/main/scripts/preprocess_dataset.py) in the code repository, from a single upstream source:

- **Stanford CGM Study** (Metwally et al. 2025, *Nature Biomedical Engineering*) — data distributed through the [`Metabolic_Subphenotype_Predictor`](https://github.com/aametwally/Metabolic_Subphenotype_Predictor) repository under the MIT license.

Cohort assignment is based on the `exp_type` column in `filtered_metabolic_tests.csv`:

- Subjects with `exp_type = venous_without_matching_cgm_and_without_planned_athome_cgm` → **initial cohort**.
- Subjects with `exp_type = venous_with_matching_cgm_and_with_planned_athome_cgm` → **validation cohort**.
- Subjects appearing in both groups are removed from the validation cohort to keep them subject-disjoint.

All glucose trajectories were smoothed onto a 5-min grid via cubic smoothing splines (`scipy.interpolate.make_smoothing_spline(lam=0.35)`); sensor `"Low"`/`"High"` strings were replaced with the empirical numeric min/max.

## Intended use

- Linear-probe / fine-tuning evaluation of CGM encoders on metabolic-subphenotype prediction.
- Cross-cohort generalization and cross-modality transfer experiments.
- Method comparison on a small but clinically labeled CGM corpus.

## License & attribution

Released under the **MIT license**, inherited from the upstream [`Metabolic_Subphenotype_Predictor`](https://github.com/aametwally/Metabolic_Subphenotype_Predictor) repository (Metwally et al. 2025, *Nature Biomedical Engineering*). Please cite both the original Stanford study and our CGM-JEPA paper when using these splits.

## Citation

```bibtex
@article{muhammad2026cgm,
  title   = {CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining},
  author  = {Muhammad, Hada Melino and Li, Zechen and Salim, Flora and Metwally, Ahmed A},
  journal = {arXiv preprint arXiv:2605.00933},
  year    = {2026}
}
```

## Code repository

[https://github.com/cruiseresearchgroup/CGM-JEPA](https://github.com/cruiseresearchgroup/CGM-JEPA)