OpenMHC DLinear
Reference checkpoint for OpenMHC. Window: daily (1440 minute), 19 sensor channels.
Wrapper around a PyPOTS-backed imputer trained on the OpenMHC imputation training split. Reconstructs masked sensor windows across 19 channels.
Intended use
Intended use: research, methodological comparison, and reproduction of the OpenMHC benchmark.
Out of scope: diagnosis, screening, prognosis, prospective risk prediction, clinical decision support, or any other medical decision-making. OpenMHC's medical condition labels are self-reported and have no diagnosis dates, and benchmark performance does not establish clinical utility. Any diagnostic, prognostic, or screening claim requires independent validation using temporally aligned, clinically adjudicated outcomes in the target population.
Usage
from openmhc.imputers import DLinearImputer
import openmhc
imp = DLinearImputer.from_release("hf://MyHeartCounts/openmhc-dlinear-imp")
results = openmhc.evaluate_imputation(imp, version="xs")
print(results.summary())
Requires the matching optional extras: pip install 'openmhc[pypots,hf]'.
Pin a specific revision with the @ suffix:
DLinearImputer.from_release("hf://MyHeartCounts/openmhc-dlinear-imp@v1.0")
Provenance
- Origin: W&B artifact
MHC_Dataset/mhc-pypots-dlinear/dlinear:v49 - Validation: val MAE 0.1335 (epoch 2)
- Architecture hyperparameters are pinned in
openmhc_manifest.json.
License
Released under the BigScience OpenRAIL-M license. The use restrictions in its Attachment A apply to this checkpoint and any derivatives; among other things, they prohibit providing medical advice and medical results interpretation.
Citation
@misc{openmhc,
title = {OpenMHC: Accelerating the Science of Wearable Foundation Models},
author = {OpenMHC team},
url = {https://github.com/AshleyLab/OpenMHC}
}