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..
The Graph-based Mutual Attentive Network for Automatic Diagnosis
Authors: Quan Yuan, Jun Chen, Chao Lu, Haifeng Huang
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The evaluation conducted on the real EMR documents demonstrates that the proposed model is more accurate compared to the previous sequence learning based diagnosis models. |
| Researcher Affiliation | Industry | Quan Yuan , Jun Chen , Chao Lu and Haifeng Huang Baidu Inc, Beijing, China EMAIL |
| Pseudocode | No | The paper describes the GMAN model and its components (medical graph construction, GCN encoding, mutual attentive network) in text, but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper mentions external open-source tools (Jieba, Cli NER) but does not provide an explicit statement or link for the source code of the GMAN model developed in this paper. |
| Open Datasets | Yes | For the reproducibility concerns, we choose MIMIC-III-50 [Mullenbach et al., 2018] as the English dataset in the evaluation besides the Chinese datasets. For MIMIC-III-50, we use the same training and testing sets from the original study4. The public English NER for clinical notes, Cli NER5, is used to process MIMIC-III-50, which reports 83.8% F1 score in the original paper [Boag et al., 2018]. |
| Dataset Splits | No | The paper provides training and testing sample counts in Table 2, but does not explicitly mention validation dataset splits, percentages, or methods. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like 'Jieba' and 'Cli NER' but does not specify their version numbers for reproducibility. |
| Experiment Setup | No | The paper mentions some configuration details like 'k = 5' for graph pruning, but lacks comprehensive experimental setup details such as specific hyperparameter values (e.g., learning rate, batch size, optimizer) for training the models. |