Federated Patient Hashing
Authors: Jie Xu, Zhenxing Xu, Peter Walker, Fei Wang6486-6493
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on real-world clinical data set from critical care are provided to demonstrate the effectiveness of the proposed method on similar patient matching across institutions. |
| Researcher Affiliation | Collaboration | 1Weill Cornell Medical College, Cornell University, USA 2U.S. Department of Defense Joint Artificial Intelligence Center, USA |
| Pseudocode | Yes | Algorithm 1 Centralized Learning Strategy to Minimize Eq. (1) [...] Algorithm 2 Decentralized Learning Strategy to Minimize Eq. (1) |
| Open Source Code | No | The paper does not provide concrete access to source code, such as a specific repository link, an explicit code release statement, or code in supplementary materials for the methodology described. |
| Open Datasets | Yes | We evaluate the proposed model over EHR data acquired from Medical Information Mart for Intensive Care III (MIMIC-III) (Johnson et al. 2016). MIMIC-III is a freely and publicly-available database which encompasses a diverse and very large population of Intensive Care Unit (ICU) patients. |
| Dataset Splits | No | For all hashing function learners, the dataset is splited into 5 folds based on sample proportion, where 4 folds are used for training and 1 fold for testing. The paper describes a 5-fold cross-validation setup with training and testing splits, but does not explicitly mention a separate validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | In the experiments, we set λ1 = λ2 = 0.5, the other regularization parameters are tuned from range {10 3, 10 2, 10 1, 1, 10, 102, 103}. |