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..
Forecasting Potential Diabetes Complications
Authors: Yang Yang, Walter Luyten, Lu Liu, Marie-Francine Moens, Jie Tang, Juanzi Li
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed model on a large collection of real medical records. Sparse FGM outperforms (+20% by F1) baselines significantly and gives detailed associations between diabetes complications and lab tests. In this section, we present experimental results to demonstrate the effectiveness of the proposed approach. |
| Researcher Affiliation | Academia | Yang Yang Tsinghua University EMAIL Walter Luyten Katholieke Universiteit Leuven EMAIL Lu Liu Northwestern University EMAIL Marie-Francine Moens Katholieke Universiteit Leuven EMAIL Jie Tang Tsinghua University EMAIL Juanzi Li Tsinghua University EMAIL |
| Pseudocode | Yes | Algorithm 1: Learning algorithm of Sparse FGM. |
| Open Source Code | Yes | All codes used in the paper are publicly available 3http://arnetminer.org/diabetes |
| Open Datasets | No | We use a collection of real medical records from a famous geriatric hospital. The data set spans one year, containing 181,933 medical records corresponding to 35,525 unique patients and 1, 945 kinds of lab tests in total. |
| Dataset Splits | No | In the experiments, we randomly picked 60% of the medical records as training data and the rest for testing. |
| Hardware Specification | Yes | All algorithms were implemented in C++, and all experiments were performed on a Mac running Mac OS X with Intel Core i7 2.66 GHz and 4 GB of memory. |
| Software Dependencies | No | All algorithms were implemented in C++, and LIBSVM (Chang and Lin 2011) is employed as the classification model for complication forecasting. However, no specific version numbers for software components like C++ libraries or LIBSVM are provided. |
| Experiment Setup | Yes | In all experiments, we empirically set the number of latent variables in Sparse FGM to 100, and set η = 0.1. We employ a gradient descent algorithm to learn the parameters in FGM (Tang, Zhuang, and Tang 2011), and set the learning rate parameter as 0.1. |