openmhc-segrnn-fc

OpenMHC Forecasting — SegRNN

Track 3 (forecasting) reference checkpoint for the MyHeartCounts / OpenMHC wearable-health benchmark.

This checkpoint is a SegRNN model. SegRNN is an RNN-based long-horizon forecaster that uses segment-wise iterations and parallel multi-step decoding.

Trained from scratch on the MHC training split using the PyPOTS implementation.

• Checkpoint format: PyPOTS checkpoint (model.pypots) + standard_scaler_stats.json + training_config.json
• Forecasting task: 24-hour-ahead, 19 sensor channels, hourly resolution.

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.

Model & implementation

• PyPOTS toolkit (implementation)
• PyPOTS documentation
• SegRNN (original implementation)
• Paper: SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting (Lin et al., IEEE IoT-J 2025)

Usage

import openmhc
from openmhc.forecasters import SegRNNForecaster

# pip install "openmhc[pypots]"
fc = SegRNNForecaster.from_release("hf://MyHeartCounts/[email protected]")
results = openmhc.evaluate_forecasting(fc, version="full")

The same bundle also loads in the evaluation harness via model.release_dir=hf://MyHeartCounts/[email protected]. See openmhc_manifest.json for provenance (training run, base model, fine-tuning details) and architecture metadata.

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

If you use this checkpoint, please cite the OpenMHC benchmark and the original SegRNN work (linked above).