Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
An Iterative Self-Learning Framework for Medical Domain Generalization
Authors: Zhenbang Wu, Huaxiu Yao, David Liebovitz, Jimeng Sun
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the generalizability of SLDG across spatial and temporal data distribution shifts on two real-world public EHR datasets: e ICU and MIMIC-IV. Our results show that SLDG achieves up to 11% improvement in the AUPRC score over the best baseline. |
| Researcher Affiliation | Academia | Zhenbang Wu 1 Huaxiu Yao 2 David M Liebovitz 3 Jimeng Sun 1 1 University of Illinois Urbana-Champaign, EMAIL 2 University of North Carolina at Chapel Hill, EMAIL 3 Northwestern University, EMAIL |
| Pseudocode | Yes | The pseudocode of SLDG can be found in Appx. A. Algorithm 1: Training and Inference for SLDG. |
| Open Source Code | No | The paper does not contain any explicit statement about making their code publicly available, nor does it provide a link to a code repository for their implementation. |
| Open Datasets | Yes | We evaluate SLDG on two publicly available real-world EHR datasets: e ICU [38] and MIMIC-IV [18] |
| Dataset Splits | Yes | Each group is then split into 70% for training, 10% for validation, and 20% for testing. |
| Hardware Specification | Yes | The model is trained on a Cent OS Linux 7 machine with 128 AMD EPYC 7513 32-Core Processors, 512 GB memory, and eight NVIDIA RTX A6000 GPUs. |
| Software Dependencies | Yes | We implement SLDG using Py Torch [34] 1.11 and Python 3.8. For SLDG, UMAP [30] from UMAPlearn [41] is used with 2 components, 10 neighbors, and 0 minimum distance; and k-Means from Scikit-learn [35] is used with the default hyper-parameter. |
| Experiment Setup | Yes | All models are trained for 100 epochs, and the best model is selected based on the AUPRC score monitored on the source validation set. For SLDG, UMAP [30] from UMAPlearn [41] is used with 2 components, 10 neighbors, and 0 minimum distance; and k-Means from Scikit-learn [35] is used with the default hyper-parameter. We apply a dropout of rate 0.2. We use Adam as the optimizer with a learning rate of 1e-4 and a weight decay of 1e-5. The batch size is 256. |