Hao Wang, Xiaodan Liang, Wenjie Li, Yefeng Zheng, Jianheng Tang, Yi Cheng, Wenge Liu, Yafei Liu, Ruihui Zhao
We lifted 5 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| lwgkzl/BR-Agent | canonical | 4 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| Mean_data | Ran | lwgkzl/BR-Agent/Environment/ExtendMaskEnvrionment.py code served (permissive licence) · get_code("967327f30d71d68d") |
| extend_dialog | Ran | lwgkzl/BR-Agent/Environment/ExtendMaskEnvrionment.py code served (permissive licence) · get_code("bb7d256baafdca5f") |
| is_nan | Ran | lwgkzl/BR-Agent/a2c/ActorNet.py code served (permissive licence) · get_code("9c41fbcc25edf379") |
| is_negative | Ran | lwgkzl/BR-Agent/a2c/ActorNet.py code served (permissive licence) · get_code("8bc52c4f68ba433b") |
| Mean_data | Not yet run | lwgkzl/BR-Agent/Environment/MeanMaskEnvrionment.py code served (permissive licence) · get_code("9beef5fe64466804") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
With the rise of telemedicine, the task of developing Dialogue Systems for Medical Diagnosis (DSMD) has received much attention in recent years. Different from early researches that needed to rely on extra human resources and expertise to help construct the system, recent researches focused on how to build DSMD in a purely data-driven manner. However, the previous data-driven DSMD methods largely overlooked the system interpretability, which is critical for a medical application, and they also suffered from the data sparsity issue at the same time. In this paper, we explore how to bring interpretability to data-driven DSMD. Specifically, we propose a more interpretable decision process to implement the dialogue manager of DSMD by reasonably mimicking real doctors' inquiry logics, and we devise a model with highly transparent components to conduct the inference. Moreover, we collect a new DSMD dataset, which has a much larger scale, more diverse patterns and is of higher quality than the existing ones. The experiments show that our method obtains 7.7%, 10.0%, 3.0% absolute improvement in diagnosis accuracy respectively on three datasets, demonstrating the effectiveness of its rational decision process and model design. Our codes and the GMD-12 dataset are available at https://github.com/lwgkzl/BR-Agent.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2204.13953")
get_code_for_paper("2204.13953")
have("2204.13953")
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