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..
Multi-TA: Multilevel Temporal Augmentation for Robust Septic Shock Early Prediction
Authors: Hyunwoo Sohn, Kyungjin Park, Baekkwan Park, Min Chi
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Validated on two real-world EHRs for septic shock, Multi-TA outperforms mixup and GAN-based stateof-the-art models across eight prediction windows, demonstrating its temporal robustness. |
| Researcher Affiliation | Collaboration | Hyunwoo Sohn1 , Kyungjin Park2 , Baekkwan Park3 and Min Chi1 1North Carolina State University 2Integral Ad Science 3University of Missouri |
| Pseudocode | Yes | Algorithm 1: Trajectory-level Augmentation |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | No | We utilize two real-world EHR datasets collected from 210,289 and 106,844 adult patient visits to Christiana Care Health System (CCHS) and Mayo Clinic (Mayo), respectively. Both datasets comprise 2.5 years (07/2013-12/2015) of anonymized and institutional review board (IRB)-approved EHRs. |
| Dataset Splits | Yes | Regular is a commonly used test set for early prediction, in which the numbers of patients in training, validation, and test sets decrease as prediction hour n increases. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like BERT and LSTM but does not provide specific version numbers for any software libraries or dependencies used in the experiments. |
| Experiment Setup | No | The paper mentions that 'Hyperparameters, such as p and U, are chosen based on the validation performance' and lists 'learning rate η' as a parameter in Algorithm 1, but it does not provide the concrete numerical values for these or other training parameters like batch size, number of epochs, or optimizer settings. |