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 [1].
ICU Mortality Prediction: A Classification Algorithm for Imbalanced Datasets
Authors: Sakyajit Bhattacharya, Vaibhav Rajan, Harsh Shrivastava
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on a benchmark dataset of 4000 ICU patients show that our algorithm surpasses the best competing methods for mortality prediction. We analyze our algorithm s performance on synthetic data with controlled overlap and imbalance and demonstrate improvement over state-of-the-art methods for ICU mortality prediction on a benchmark dataset of 4000 patients. |
| Researcher Affiliation | Industry | Sakyajit Bhattacharya, Vaibhav Rajan, Harsh Shrivastava Xerox Research Centre India, Bangalore, India |
| Pseudocode | Yes | Algorithm 1 CHISQ Classification Algorithm |
| Open Source Code | No | The paper does not provide any statement about making its source code publicly available or link to a code repository. |
| Open Datasets | Yes | We use the publicly available labeled dataset of 4000 patients from the Physionet 2012 challenge (Silva et al. 2012) which is from the the MIMIC II ICU database (Goldberger et al. 2000). |
| Dataset Splits | Yes | For each setting, we use 80% of the dataset, chosen randomly, for training and the remaining as test set. Table 3 shows the 4-fold cross-validation (CV) scores reported by the top three challenge winners on Training Set A. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using MATLAB for simulation but does not specify its version or other software dependencies with version numbers. |
| Experiment Setup | Yes | PREDICTION (Given test sample Z, significance level α). For CSL, we set the weight in inverse ratio of the number of training samples, i.e., weight of a sample from class C1 is n C2/n C1 where n C1 (n C2) is the number of training samples in class C1 (C2). |