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..
ATTAIN: Attention-based Time-Aware LSTM Networks for Disease Progression Modeling
Authors: Yuan Zhang, Xi Yang, Julie Ivy, Min Chi
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate ATTAIN on modeling the progression of an extremely challenging disease, septic shock, by using real-world EHRs. Our results demonstrate that the proposed framework outperforms the state-of-the-art models such as RETAIN and T-LSTM. |
| Researcher Affiliation | Academia | Yuan Zhang1 , Xi Yang1 , Julie Ivy2 and Min Chi1 1Computer Science, North Carolina State University 2Industrial and System Engineering, North Carolina State University 1EMAIL, EMAIL |
| Pseudocode | No | The paper provides mathematical equations and descriptions of the model architecture but does not include pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of its source code. |
| Open Datasets | No | Our EHR data was collected from Christiana Care Health System Health System (CCHS) from July, 2013 to December, 2015. |
| Dataset Splits | Yes | In training process, we randomly divide the data sets into the training, validation and testing set with the ratio of 70%, 15%, and 15%. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU models, CPU types) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | The training epochs is 50 with early stopping, the learning rate is 0.01, and the number of hidden units for LSTM is 72. |