[CS598 DL4H] Added mortality_prediction_eicu.py, mortality_readmission_los_eicu_mlp.py, test_mortality_prediction_eicu.py, and pyhealth.tasks.mortality_prediction_eicu.rst; removed patient_readmission.py, patient_readmission.eicu.py, test_patient_readmission.py, and pyhealth.tasks.patient_readmission.rst. - #1073
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[CS598 DL4H] Added mortality_prediction_eicu.py, mortality_readmission_los_eicu_mlp.py, test_mortality_prediction_eicu.py, and pyhealth.tasks.mortality_prediction_eicu.rst; removed patient_readmission.py, patient_readmission.eicu.py, test_patient_readmission.py, and pyhealth.tasks.patient_readmission.rst.#1073dylan-g12 wants to merge 11 commits into
dylan-g12 wants to merge 11 commits into
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…alth/pyhealth/tasks and test_patient_readmission.py from .github to PyHealth/tests
…in patient_readmission.py to not copy class names
…es pyhealth.tasks.BaseTask =======================================
…ient_readmission.py, and pyhealth.tasks.patient_readmission.rst; added mortality_prediction_eicu.py, mortaility_readmission_los_predictions_eicu_mlp.py, test_mortaility_prediction_eicu.py, and pyhealth.tasks.mortality_prediction_eicu.rst with modifications to tasks.rst. Reason: misunderstanding of reproduction.
Jathurshan0330
self-requested a review
May 3, 2026 20:08
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PR is good and follows the guidelines well |
Jathurshan0330
approved these changes
May 3, 2026
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This PR has been automatically marked as stale because it has not had recent activity. It will be closed in 7 days if no further activity occurs. |
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This PR has been automatically closed due to inactivity. Please re-open it if you think it is still relevant. |
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Contributor:
Dylan Guo (dylanlg3@illinois.edu)
Paper Title and Link:
A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs (https://arxiv.org/abs/2504.14657)
Contribution Type:
New Task Contribution
Description:
This PR adds a task as described by Lin, Yu, and Lee in A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs that predicts the mortality of patients based on patient features from the eICU database. With test cases for the reproduced task and an ablation comparing mortality, readmission, and LOS prediction task effectiveness based on the number of features.
The task performs the following:
Files to Review: