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..
Federated Patient Hashing
Authors: Jie Xu, Zhenxing Xu, Peter Walker, Fei Wang6486-6493
AAAI 2020 | Venue PDF | 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}. |