MSDiagnosis: An EMR-based Dataset for Clinical Multi-Step Diagnosis
<|reference_start|>MSDiagnosis: An EMR-based Dataset for Clinical Multi-Step Diagnosis: Clinical diagnosis is critical in medical practice, typically requiring a continuous and evolving process that includes primary diagnosis, differential diagnosis, and final diagnosis. However, most existing clinical diagnostic tasks are single-step processes, which does not align with the complex multi-step diagnostic procedures found in real-world clinical settings. In this paper, we propose a multi-step diagnostic task and annotate a clinical diagnostic dataset (MSDiagnosis). This dataset includes primary diagnosis, differential diagnosis, and final diagnosis questions. Additionally, we propose a novel and effective framework. This framework combines forward inference, backward inference, reflection, and refinement, enabling the LLM to self-evaluate and adjust its diagnostic results. To assess the effectiveness of our proposed method, we design and conduct extensive experiments. The experimental results demonstrate the effectiveness of the proposed method. We also provide a comprehensive experimental analysis and suggest future research directions for this task.<|reference_end|>
@article{hou2024msdiagnosis:,
    title={MSDiagnosis: An EMR-based Dataset for Clinical Multi-Step Diagnosis},
    author={Ruihui Hou, Shencheng Chen, Yongqi Fan, Lifeng Zhu, Jing Sun, Jingping
  Liu and Tong Ruan},
    journal={arXiv preprint arXiv:2408.10039},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10039},
    primaryClass={cs.AI}
}