Hierarchical Apprenticeship Learning for Disease Progression Modeling
Authors: Xi Yang, Ge Gao, Min Chi
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the efficacy of this approach in a challenging task of septic shock early prediction, and our results demonstrate that integrating the AL-derived patterns significantly enhances the performance of DPM. |
| Researcher Affiliation | Collaboration | Xi Yang1 , Ge Gao2 , Min Chi2 1 IBM Research 2 North Carolina State University xi.yang@ibm.com, {ggao5, mchi}@ncsu.edu |
| Pseudocode | No | The paper describes algorithmic steps but does not include structured pseudocode or algorithm blocks with explicit labels like 'Algorithm' or 'Pseudocode'. |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the described methodology or a link to a code repository. |
| Open Datasets | No | To assess the effectiveness of THEMES-derived patterns for DPM, we applied it to an EHRs dataset obtained from the Christiana Care Health System. |
| Dataset Splits | Yes | Each method was repeated 10 times, with the data randomly divided into 80% for training and 20% for testing. All parameters in THEMES are determined by 5-fold cross-validation. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments, such as specific GPU or CPU models. |
| Software Dependencies | No | The paper mentions 'We utilized Keras to implement the LSTM' but does not provide specific version numbers for Keras or any other software dependencies. |
| Experiment Setup | Yes | We utilized Keras to implement the LSTM and performed parameter tuning through grid search. ... In RMT-TICC, the cluster number K is set to 11 based on Bayesian information criteria (BIC) [Friedman et al., 2001], the window size ω is set to 2, and the sparsity λ and consistency β coefficients are set to 1e-5 and 4, respectively. In EM-EDM, the cluster number is determined heuristically as 3, by iteratively applying the EM algorithm until empty clusters are generated or the log-likelihood varies less than a predefined threshold. The THEMES approach uses a threshold of 10 iterations |