Labrador model ml171#949
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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.
jhnwu3
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Apr 27, 2026
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Hi John,
Sorry for late reply, I'll take a look tomorrow.
Thanks,
Mark
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From: John Wu ***@***.***>
Sent: April 27, 2026 3:24 PM
To: sunlabuiuc/PyHealth ***@***.***>
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Subject: Re: [sunlabuiuc/PyHealth] Labrador model ml171 (PR #949)
@jhnwu3 requested changes on this pull request.
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In LABRADOR_TEST_SUMMARY.md<#949 (comment)>:
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+# LabradorModel Test
+
+**Test File:** `tests/core/test_labrador.py`
+
+## Overview
Please remove this LLM generated set of test case summaries here.
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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