IMCS-21
收藏数据集概述
数据集名称: IMCS-21
数据集描述: IMCS-21是一个用于自动化医疗咨询系统的基准数据集，包含4,116条经过标注的医疗咨询记录，覆盖10种儿科疾病。该数据集旨在支持自动医疗咨询的研究，包括多个独立的任务，如命名实体识别、对话行为分类、症状标签推断、医疗报告生成和诊断导向的对话策略。
数据集版本
- IMCS-21 2.0: 更新版本，详细信息可访问此处。
数据集任务与基准模型
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命名实体识别 (NER):
- 基准模型: Lattice LSTM, BERT, ERNIE, FLAT, LEBERT
- 评估指标: 实体级和令牌级F1分数
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对话行为分类 (DAC):
- 基准模型: TextCNN, TextRNN, TextRCNN, DPCNN, BERT, ERNIE
- 评估指标: 精确度(P), 召回率(R), F1分数(宏观), 准确性(Acc)
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症状标签推断 (SLI):
- 基准模型: BERT-MLC, BERT-MTL
- 评估指标: 示例级指标(SA, HL, HS)和标签级指标(P, R, F1)
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医疗报告生成 (MRG):
- 基准模型: Seq2Seq, PG, Transformer, T5, ProphetNet
- 评估指标: BLEU-2/4, ROUGE-1/2/L, 概念F1分数(C-F1), 基于正则表达式的诊断准确性(RD-Acc)
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诊断导向的对话策略 (DDP):
- 基准模型: DQN, REFUEL, KR-DQN, GAMP, HRL
- 评估指标: 症状召回率(Rec), 诊断准确性(Acc), 平均交互次数(# Turns)
数据集测试集
- 测试集位置: 托管于CBLEU平台
- 提交结果: 欢迎在CBLEU平台上提交结果，或比较验证集上的结果。
引用信息
若使用此数据集，请引用以下论文:
@article{10.1093/bioinformatics/btac817, author = {Chen, Wei and Li, Zhiwei and Fang, Hongyi and Yao, Qianyuan and Zhong, Cheng and Hao, Jianye and Zhang, Qi and Huang, Xuanjing and Peng, Jiajie and Wei, Zhongyu}, title = "{A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks and Datasets}", journal = {Bioinformatics}, year = {2022}, month = {12}, abstract = "{In recent years, interest has arisen in using machine learning to improve the efficiency of automatic medical consultation and enhance patient experience. In this article, we propose two frameworks to support automatic medical consultation, namely doctor-patient dialogue understanding and task-oriented interaction. We create a new large medical dialogue dataset with multi-level fine-grained annotations and establish five independent tasks, including named entity recognition, dialogue act classification, symptom label inference, medical report generation and diagnosis-oriented dialogue policy.We report a set of benchmark results for each task, which shows the usability of the dataset and sets a baseline for future studies.Both code and data is available from https://github.com/lemuria-wchen/imcs21.Supplementary data are available at Bioinformatics online.}", issn = {1367-4803}, doi = {10.1093/bioinformatics/btac817}, url = {https://doi.org/10.1093/bioinformatics/btac817}, note = {btac817}, eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btac817/48290490/btac817.pdf}, }
