This paper focuses on term-status pair extraction from medical dialogues
(MD-TSPE), which is essential in diagnosis dialogue systems and the automatic
scribe of electronic medical records (EMRs). In the past few years, works on
MD-TSPE have attracted increasing research attention, especially after the
remarkable progress made by generative methods. However, these generative
methods output a whole sequence consisting of term-status pairs in one stage
and ignore integrating prior knowledge, which demands a deeper understanding to
model the relationship between terms and infer the status of each term. This
paper presents a knowledge-enhanced two-stage generative framework (KTGF) to
address the above challenges. Using task-specific prompts, we employ a single
model to complete the MD-TSPE through two phases in a unified generative form:
we generate all terms the first and then generate the status of each generated
term. In this way, the relationship between terms can be learned more
effectively from the sequence containing only terms in the first phase, and our
designed knowledge-enhanced prompt in the second phase can leverage the
category and status candidates of the generated term for status generation.
Furthermore, our proposed special status ``not mentioned" makes more terms
available and enriches the training data in the second phase, which is critical
in the low-resource setting. The experiments on the Chunyu and CMDD datasets
show that the proposed method achieves superior results compared to the
state-of-the-art models in the full training and low-resource settings.

本文提出了一种知识增强的两阶段生成框架（KTGF）来从医学对话中提取术语 - 状态对（MD-TSPE），以解决生成方法在集成先验知识、建模术语关系和推断术语状态方面存在的挑战。本方法在综合生成形式中通过任务特定提示完成 MD-TSPE，并在全训练和低资源环境中实现了优于现有模型的结果。

基于知识增强的医疗对话信息提取的两阶段生成框架

A Knowledge-enhanced Two-stage Generative Framework for Medical Dialogue  Information Extraction

The recent progress of large language models (LLMs), including ChatGPT and
GPT-4, in comprehending and responding to human instructions has been
remarkable. Nevertheless, these models typically perform better in English and
have not been explicitly trained for the medical domain, resulting in
suboptimal precision in diagnoses, drug recommendations, and other medical
advice. Additionally, training and deploying a dialogue model is still believed
to be impossible for hospitals, hindering the promotion of LLMs. To tackle
these challenges, we have collected databases of medical dialogues in Chinese
with ChatGPT's help and adopted several techniques to train an easy-deploy LLM.
Remarkably, we were able to fine-tune the ChatGLM-6B on a single A100 80G in 13
hours, which means having a healthcare-purpose LLM can be very affordable.
DoctorGLM is currently an early-stage engineering attempt and contain various
mistakes. We are sharing it with the broader community to invite feedback and
suggestions to improve its healthcare-focused capabilities:
this https URL

该研究旨在解决大型语言模型在医学领域的准确性问题，通过采集汉语医学对话数据库并使用一些技术训练了易于部署的的 ChatGLM-6B 模型，最终可以以较低的成本实现健康医疗目的的 LLM。

DoctorGLM：细调您的中文医生并非力大无比的任务

DoctorGLM: Fine-tuning your Chinese Doctor is not a Herculean Task

Medication recommendation is a crucial task for intelligent healthcare
systems. Previous studies mainly recommend medications with electronic health
records (EHRs). However, some details of interactions between doctors and
patients may be ignored or omitted in EHRs, which are essential for automatic
medication recommendation. Therefore, we make the first attempt to recommend
medications with the conversations between doctors and patients. In this work,
we construct DIALMED, the first high-quality dataset for medical dialogue-based
medication recommendation task. It contains 11,996 medical dialogues related to
16 common diseases from 3 departments and 70 corresponding common medications.
Furthermore, we propose a Dialogue structure and Disease knowledge aware
Network (DDN), where a QA Dialogue Graph mechanism is designed to model the
dialogue structure and the knowledge graph is used to introduce external
disease knowledge. The extensive experimental results demonstrate that the
proposed method is a promising solution to recommend medications with medical
dialogues. The dataset and code are available at
this https URL.

该研究提出一种基于医疗对话的药物推荐方法，使用自然语言处理技术结合病历数据，构建了一个高质量的医疗对话数据集 DIALMED, 并提出了 DDN 模型和 QA 对话图机制，有效考虑了各种因素的影响，实现了医患对话中对药物的智能推荐。