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Datasets:
CRUISEResearchGroup
/
CGM-JEPA-Downstream

Tasks:
Time Series Forecasting
Tabular Classification
Feature Extraction
Modalities:
Tabular
Text
Formats:
parquet
Sub-tasks:
tabular-multi-class-classification
Languages:
English
Size:
< 1K
ArXiv:
Tags:
continuous-glucose-monitor
cgm
insulin-resistance
beta-cell-dysfunction
metabolic-subphenotype
ogtt
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
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Initial release: 27+17 Stanford subjects, IR + β labels

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Files changed (3) hide show
  1. README.md +151 -3
  2. train_split.json +1487 -0
  3. validation_split.json +0 -0
README.md CHANGED
@@ -1,3 +1,151 @@
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- ---
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- license: mit
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
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+ license: mit
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+ language:
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+ - en
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+ pretty_name: CGM-JEPA Downstream Evaluation Splits
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+ task_categories:
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+ - time-series-classification
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+ - feature-extraction
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+ tags:
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+ - continuous-glucose-monitor
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+ - cgm
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+ - insulin-resistance
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+ - beta-cell-dysfunction
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+ - metabolic-subphenotype
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+ - ogtt
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+ - healthcare
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+ - time-series-classification
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+ size_categories:
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+ - n<1K
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+ ---
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+
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+ # CGM-JEPA Downstream Evaluation Splits
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+
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+ 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*.
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+
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+ > 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-Weights`](https://huggingface.co/CRUISEResearchGroup/CGM-JEPA-Weights).
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+
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+ ## Quick start
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+
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+ ```bash
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+ huggingface-cli download CRUISEResearchGroup/CGM-JEPA-Downstream \
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+ --repo-type dataset --local-dir Dataset_Open
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+ ```
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+
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+ Then from the [code repository](https://github.com/cruiseresearchgroup/CGM-JEPA):
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+
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+ ```bash
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+ # Reproduce all 3 evaluation regimes × 2 endpoints (Tables 1–6)
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+ python scripts/run_all_eval.py
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+ ```
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+
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+ ## Files
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+
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+ | File | Subjects | Size | Role |
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+ |---|---|---|---|
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+ | `train_split.json` | 27 | ~45 KB | **Initial cohort** — used to train linear probes in the cohort-generalization regime |
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+ | `validation_split.json` | 17 | ~146 KB | **Validation cohort** — used as the test set across all regimes and as both train and test in the home-CGM in-domain regime |
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+
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+ The two cohorts are **subject-disjoint by construction**: subjects appearing in both upstream groups were removed from the validation cohort during preprocessing.
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+
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+ ## Schema
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+
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+ Both files use the same nested-JSON structure:
54
+
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+ ```jsonc
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+ {
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+ "S01": { // subject identifier
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+ "x": {
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+ "ctru_venous": [<float>, …], // sequence of glucose values (mg/dL)
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+ "ctru_cgm": [<float>, …], // (validation cohort only)
61
+ "home_cgm_1": [<float>, …], // "
62
+ "home_cgm_2": [<float>, …], // "
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+ "cgm_home_mean":[<float>, …], // mean of home_cgm_1 & home_cgm_2
64
+ "cgm_all_mean": [<float>, …] // mean of ctru_cgm, home_cgm_1, home_cgm_2
65
+ },
66
+ "y": {
67
+ "ir": {"class": 0|1, "regression": <float>}, // SSPG-derived
68
+ "beta": {"class": 0|1, "regression": <float>} // DI-derived
69
+ }
70
+ },
71
+ "S02": { ... },
72
+ ...
73
+ }
74
+ ```
75
+
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+ ### Extract methods (`x` sub-keys)
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+
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+ | Key | Availability | Description |
79
+ |---|---|---|
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+ | `ctru_venous` | train + validation | In-clinic venous OGTT glucose trajectory |
81
+ | `ctru_cgm` | validation only | In-clinic CGM trajectory recorded during the same OGTT |
82
+ | `home_cgm_1` | validation only | First free-living home-CGM window |
83
+ | `home_cgm_2` | validation only | Second free-living home-CGM window |
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+ | `cgm_home_mean` | validation only | Subject-level mean of `home_cgm_1` and `home_cgm_2` |
85
+ | `cgm_all_mean` | validation only | Subject-level mean of all three CGM modalities |
86
+
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+ 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.
88
+
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+ ### Labels (`y` sub-keys)
90
+
91
+ | Field | Type | Source | Threshold |
92
+ |---|---|---|---|
93
+ | `ir.class` | binary {0, 1} | SSPG (Steady-State Plasma Glucose) | 1 = insulin-resistant, 0 = insulin-sensitive |
94
+ | `ir.regression` | float | SSPG numeric value | mg/dL |
95
+ | `beta.class` | binary {0, 1} | DI (Disposition Index) | 1 = β-cell dysfunction, 0 = normal β-cell function |
96
+ | `beta.regression` | float | DI numeric value | dimensionless |
97
+
98
+ A class value of `-1` indicates a missing or unannotated label. Threshold definitions follow Metwally et al. (2025).
99
+
100
+ ### Class distribution
101
+
102
+ | Cohort | n | IR=1 (resistant) | IR=0 (sensitive) | β=1 (dysfunction) | β=0 (normal) |
103
+ |---|---:|---:|---:|---:|---:|
104
+ | Initial (train_split) | 27 | 14 | 13 | 16 | 11 |
105
+ | Validation (validation_split) | 17 | 7 | 10 | 6 | 11 |
106
+
107
+ Both labels are reasonably balanced; the paper reports stratified 2-fold cross-validation over 20 random iterations (40 paired evaluations per cell).
108
+
109
+ ## Evaluation regimes (paper Tables 1–6)
110
+
111
+ The two splits support all three deployment regimes evaluated in the paper:
112
+
113
+ | Regime | Train on | Test on |
114
+ |---|---|---|
115
+ | Cohort generalization (venous) | `train_split` × `ctru_venous` | `validation_split` × `ctru_venous` |
116
+ | Venous → home-CGM transfer | `validation_split` × `ctru_venous` | `validation_split` × `cgm_home_mean` |
117
+ | In-domain home CGM | `validation_split` × `cgm_home_mean` | `validation_split` × `cgm_home_mean` |
118
+
119
+ All regimes are orchestrated by [`scripts/run_all_eval.py`](https://github.com/cruiseresearchgroup/CGM-JEPA/blob/main/scripts/run_all_eval.py).
120
+
121
+ ## How this corpus was built
122
+
123
+ 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:
124
+
125
+ - **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.
126
+
127
+ Cohort assignment is based on the `exp_type` column in `filtered_metabolic_tests.csv`:
128
+
129
+ - Subjects with `exp_type = venous_without_matching_cgm_and_without_planned_athome_cgm` → **initial cohort**.
130
+ - Subjects with `exp_type = venous_with_matching_cgm_and_with_planned_athome_cgm` → **validation cohort**.
131
+ - Subjects appearing in both groups are removed from the validation cohort to keep them subject-disjoint.
132
+
133
+ 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.
134
+
135
+ ## Intended use
136
+
137
+ - Linear-probe / fine-tuning evaluation of CGM encoders on metabolic-subphenotype prediction.
138
+ - Cross-cohort generalization and cross-modality transfer experiments.
139
+ - Method comparison on a small but clinically labeled CGM corpus.
140
+
141
+ ## License & attribution
142
+
143
+ 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.
144
+
145
+ ## Citation
146
+
147
+ > _Citation block to be filled once the CGM-JEPA paper has a stable venue / arXiv link._
148
+
149
+ ## Code repository
150
+
151
+ [`https://github.com/cruiseresearchgroup/CGM-JEPA`](https://github.com/cruiseresearchgroup/CGM-JEPA)
train_split.json ADDED
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validation_split.json ADDED
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