pandalla/datatager_symptom_recognition_and_advice

---
license: apache-2.0
---

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# Extract Medical Information Dataset

## Prompt for Training
When training your model with this dataset, prepend the following prompt to each input instance:

```
你需要去做的是理解患者的咨询文本，并基于这些症状提供一个可能的医学解释以及相应的建议措施。请始终确保你的输出中包括以下元素：1. 对输入中提到的症状的识别和确认。2. 基于症状的可能医学解释。3. 针对进一步诊断或治疗的建议措施。
```

## Description
AnyTaskTune is a publication by the DataTager team. We advocate for rapid training of large models suitable for specific business scenarios through task-specific fine-tuning. We have open-sourced several datasets across various domains such as legal, medical, education, and HR, and this dataset is one of them.

Symptom Recognition and Advice is a publication by the DataTager team. This dataset aims to enhance the capabilities of AI systems in medical consultation by enabling the recognition of symptoms and delivery of tailored medical advice based on patient inquiries. It provides extensive data derived from real patient interactions, encompassing a wide range of symptoms and conditions.

## Usage

This dataset is an invaluable resource for developing AI models that can accurately identify symptoms and provide appropriate advice during medical consultations. Utilizing this dataset, AI systems can be trained to not only recognize various health conditions but also suggest actionable medical advice, thereby assisting healthcare professionals to assess patient needs more effectively and expediently. The dataset can also serve educational purposes, training medical students to identify critical information swiftly during patient interactions.

## Citation

Please cite this dataset in your work as follows:

```
@misc{ Extract Medical Information Dataset,
author = {DataTager},
title = {Extract Medical Information Dataset},
year = {2024},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\url{https://github.com/PandaVT/DataTager}}
}
```

许可证：Apache-2.0

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# 医疗信息抽取数据集

## 训练提示词
当使用本数据集训练模型时，请在每个输入样本前添加如下提示词：

你需要去做的是理解患者的咨询文本，并基于这些症状提供一个可能的医学解释以及相应的建议措施。请始终确保你的输出中包括以下元素：1. 对输入中提到的症状的识别和确认。2. 基于症状的可能医学解释。3. 针对进一步诊断或治疗的建议措施。

## 数据集说明
AnyTaskTune是DataTager团队发布的一项研究成果。我们倡导通过面向任务的微调，快速训练适配特定业务场景的大语言模型（Large Language Model）。我们已开源多个覆盖法律、医疗、教育、人力资源等多领域的数据集，本数据集即为其中之一。

"症状识别与建议"是DataTager团队发布的另一项研究成果。本数据集旨在通过赋能AI系统识别患者咨询中的症状，并据此生成定制化医疗建议，以提升其在医疗咨询场景下的能力。数据集包含大量源自真实患者交互的详实数据，涵盖了丰富的症状与病症类型。

## 使用说明
本数据集是开发AI模型的宝贵资源，此类模型可在医疗咨询场景中精准识别症状并提供恰当建议。通过本数据集训练AI系统，不仅可使其识别各类健康状况，还能给出可落地的医疗建议，从而协助医疗从业者更高效、快捷地评估患者需求。此外，本数据集还可用于教育场景，帮助医学生在患者交互过程中快速识别关键信息。

## 引用格式
请在您的研究工作中按如下格式引用本数据集：

@misc{ Extract Medical Information Dataset,
author = {DataTager},
title = {Extract Medical Information Dataset},
year = {2024},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {url{https://github.com/PandaVT/DataTager}}
}