Predicting Postoperative Atrial Fibrillation from Independent ECG Components
Authors: Chih-Chun Chia, James Blum, Zahi Karam, Satinder Singh, Zeeshan Syed
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | When evaluated on 385 patients undergoing cardiac surgery, this approach of stratifying patients for PAF through an analysis of morphologic variability within decoupled atrial ECG demonstrated substantial promise and improved net reclassification by over 53% relative to the use of baseline clinical characteristics. We evaluated our proposed methodology for PAF prediction on both synthetic and real-world data. |
| Researcher Affiliation | Academia | Chih-Chun Chia, James Blum, Zahi Karam, Satinder Singh, Zeeshan Syed University of Michigan, Ann Arbor, MI |
| Pseudocode | No | The paper describes mathematical formulations and steps for its algorithms (ICA, SEM) in prose and equations, but it does not include any structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about the release of open-source code for the methodology described, nor does it include links to a code repository. |
| Open Datasets | No | Data from 385 patients undergoing CABG, aortic, or other valvular surgeries at the University of Michigan Hospital were collected in 2013. |
| Dataset Splits | No | The paper states 'randomly divided data into 50% training and 50% test sets' and refers to 'cross-validated AUC results', but it does not specify an explicit separate validation dataset split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as CPU or GPU models, or cloud computing specifications. |
| Software Dependencies | No | The paper mentions methods and algorithms but does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper describes the models used (logistic regression with stepwise backward elimination) and the features included, but it does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or specific optimizer settings. |