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configs:
- config_name: cohort
data_files:
- split: train
path: cohort_subset.csv
- config_name: covariates
data_files:
- split: train
path: covariate_subset.csv
- config_name: data
data_files:
- split: train
path: data_subset.csv
---
# OMOP POU Data Card
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## Publication
Naderalvojoud, Behzad, et al.
"Towards global model generalizability: independent cross-site feature evaluation for patient-level risk prediction models using the OHDSI network."
*Journal of the American Medical Informatics Association* 31.5 (2024): 1051–1061.
Please refer to the publication for full details on cohort construction and model development.
---
## Dataset Specification
- **Source of Data**: STARR OMOP
- **Prediction outcome**: Prolonged opioid use
- 1: Prolonged
- 0: Non-prolonged
- **Covariate count**: 10,636
- **Total cohort size (original)**: 41,929 patients
- **Final modeling cohort**: 41,507 patients
- **Training set size**: 33,206
- **Test set size**: 8,301
---
## Note on Data Availability
Due to PHI restrictions, the full dataset cannot be publicly released. The files provided here are **small subset examples with synthetic patients**, intended only to demonstrate the **structure and format** of the dataset.
---
## File Descriptions
### `cohort_subset.csv`
Contains 10 example (synthetic) patients with:
- `rowId`: patient identifier
- `outcome`: prolonged opioid use label
---
### `data_subset.csv`
Represents the model input matrix:
- Each row corresponds to a patient
- Each column corresponds to a covariate (by covariateId)
- `rowId` links to `cohort_subset.csv`
---
### `covariate_subset.csv`
Covariate dictionary containing:
- `covariateId`
- `covariateName` |