The Graph-based Mutual Attentive Network for Automatic Diagnosis
Authors: Quan Yuan, Jun Chen, Chao Lu, Haifeng Huang
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | 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 {yuanquan02, chenjun22, luchao, huanghaifeng}@baidu.com |
| 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. |