Multi-TA: Multilevel Temporal Augmentation for Robust Septic Shock Early Prediction
Authors: Hyunwoo Sohn, Kyungjin Park, Baekkwan Park, Min Chi
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | 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. |