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- Added a new section demonstrating masking of lab positions using the LabradorModel encoder. - Implemented functions for masking, encoding, and computing metrics for masked predictions. - Updated evaluation metrics to include precision, recall, and F1 score for masked code predictions. - Adjusted execution counts and markdown headers for clarity and consistency.
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Contributor: Mark Lee (ml171@illinois.edu)
Contribution type: Model
Description:
Implementation of a Transformer model for structured laboratory data in PyHealth from the Labrador Paper (https://arxiv.org/abs/2312.11502). The model processes aligned lab code (categorical) and lab value (continuous) inputs, applies a Transformer encoder without positional encoding, and performs downstream classification.
Files to review
pyhealth/models/labrador.py
pyhealth/models/init.py
tests/test_labrador.py
docs/api/models/pyhealth.models.labrador.rst
docs/api/models.rst
examples/labrador_ablations_quickstart.ipynb