mdplus/Datathon2024

--- license: mit task_categories: - question-answering - text-classification - summarization - text-generation language: - en tags: - medical pretty_name: MDplus Datathon 2024 Datasets size_categories: - 10K<n<100K --- ### Dataset Description The **3rd annual MD+ datathon** is a national month-long event hosted by MD+ and sponsors to foster innovative thinking about complex healthcare problems and their data-driven solutions. Medical students, graduate students, and trainees from all levels work together across disciplines to generate insights and engineer solutions from patient datasets. In contrast to prior years, the 2024 MD+ Datathon will be divided into 3 separate competition tracks, each using a different publicly available dataset. The overarching theme of this year's Datathon is **Responsible Generative AI for Clinical Care**. This is a purposely broad topic and teams are encouraged to explore potential use cases of generative AI and machine learning as they pertain to clinical problems in mental healthcare, clinical documentation, and medical education. #### Mental Health Track - [TW: SI, mention of su*cide in some dataset rows] - **Repository:** [Reddit SuicideWatch and Mental Health Collection (SWMH) for Suicidal Ideation and Mental Disorder Detection](https://zenodo.org/records/6476179) - **Paper**: [Ji S et al. Suicidal ideation and mental disorder detection with attentive relation networks. Neur Comp App 34: 10309-19. (2021). doi: 10.1007/s00521-021-06208-y](https://doi.org/10.1007/s00521-021-06208-y) #### Clinical Documentation Track - **Repository:** [`microsoft/clinical_visit_note_summarization_corpus`](https://github.com/microsoft/clinical_visit_note_summarization_corpus/tree/main) - **Paper**: [Ben Abacha A et al. An empirical study of clinical note generation from doctor-patient encounters. Proc Conf Assoc Comp Ling: 2291-302. (2023). doi: 10.18653/v1/2023.eacl-main.168](https://aclanthology.org/2023.eacl-main.168/) #### Medical Education Track - **Repository:** [`bigbio/med_qa`](https://huggingface.co/datasets/bigbio/med_qa) - **Paper**: [Jin D et al. What disease does this patient have? A large-scale open domain question answering dataset from medical exams. J Appl Sci 11(14): 6421. (2021). doi: 10.3390/app11146421](https://www.mdpi.com/2076-3417/11/14/6421) ### Uses **These datasets may only be used for research purposes in association with the 2024 MDplus Datathon.** Any other use cases are explicity forbidden due to data licensing requirements. ### Dataset Structure #### Mental Health Track Each row in this dataset contains 2 fields: 1. `text`: the patient-generated text scraped from the Internet 2. `label`: one of five categories: `anxiety`, `suicide_watch`, `bipolar`, `depression`, and `off_my_chest`. There are 43,529 observations in the training dataset and 10,883 observations in the test dataset. #### Clinical Documentation Track Each row in this dataset contains 11 fields: 1. `dataset`: a metadata field describing the source of this row 2. `encounter_id`: a unique identifier for each patient encounter 3. `dialogue`: an audio transcription of the encounter 4. `note`: the text note generated after the encounter 5. `doctor_name`: the name of the physician 6. `patient_gender`: the gender of the patient 7. `patient_age`: the age of the patient in years 8. `patient_firstname`: the first name of the patient 9. `patient_lastname`: the last name of the patient 10. `chief_complaint`: the chief complaint of the patient 11. `addition_complaints`: any additional complaints of the patient There are 67 observations in the training dataset and 20 observations in the test dataset. #### Medical Education Track Each row in this dataset contains 5 fields: 1. `question`: the USMLE question 2. `answer`: the correct answer text 3. `options`: a dictionary mapping each multiple-choice option to the associated answer text 4. `meta_info`: whether the question is Step 1, 2, and/or 3 5. `answer_idx`: the correct multiple-choice option There are 10,178 observations in the training dataset and 1,273 observations in the test dataset. ### Dataset Card Contact Please contact [Michael Yao](mailto:[e-mail]) with any questions or concerns.license: MIT协议 task_categories: - 问答 - 文本分类 - 文本摘要 - 文本生成 language: - 英语 tags: - 医疗 pretty_name: MDplus 2024数据竞赛数据集 size_categories: - 1万 < 样本量 < 10万 --- ### 数据集说明 **第三届MD+数据竞赛**是由MD+及其赞助商主办的全国性、为期一个月的活动，旨在推动针对复杂医疗问题及其数据驱动解决方案的创新思维。来自各个层级的医学生、研究生及受训人员将跨学科协作，从患者数据集挖掘研究洞见并设计解决方案。 与往届不同，2024年MD+数据竞赛将分为3个独立竞赛赛道，每个赛道使用一套不同的公开可用数据集。本届竞赛的核心主题为**面向临床医疗的负责任生成式人工智能（Generative AI）**。本主题刻意设置为宽泛范畴，组委会鼓励参赛团队探索生成式人工智能与机器学习在精神医疗、临床文档记录以及医学教育领域的临床相关问题中的潜在应用场景。 #### 精神健康赛道 【触发警告：自杀意念，部分数据集条目提及自杀相关内容】 - **数据集仓库**：[用于自杀意念与精神障碍检测的Reddit自杀观察与心理健康数据集（SWMH）](https://zenodo.org/records/6476179) - **相关论文**：[Ji S等人. 基于注意力关系网络的自杀意念与精神障碍检测. 神经计算与应用, 34: 10309-19. (2021). doi: 10.1007/s00521-021-06208-y](https://doi.org/10.1007/s00521-021-06208-y) #### 临床文档记录赛道 - **数据集仓库**：[`microsoft/clinical_visit_note_summarization_corpus`](https://github.com/microsoft/clinical_visit_note_summarization_corpus/tree/main) - **相关论文**：[Ben Abacha A等人. 医患对话生成临床笔记的实证研究. 计算语言学协会会议论文集, 2291-302. (2023). doi: 10.18653/v1/2023.eacl-main.168](https://aclanthology.org/2023.eacl-main.168/) #### 医学教育赛道 - **数据集仓库**：[`bigbio/med_qa`](https://huggingface.co/datasets/bigbio/med_qa) - **相关论文**：[Jin D等人. 该患者患有何种疾病？来自医学考试的大规模开放域问答数据集. 应用科学期刊, 11(14): 6421. (2021). doi: 10.3390/app11146421](https://www.mdpi.com/2076-3417/11/14/6421) ### 使用限制 本数据集仅可用于与2024 MD+数据竞赛相关的研究用途。根据数据许可协议要求，任何其他使用场景均被明确禁止。 ### 数据集结构 #### 精神健康赛道 该数据集的每一行包含2个字段： 1. `text`：从互联网爬取的患者生成文本 2. `label`：共5个分类标签之一：`anxiety`（焦虑）、`suicide_watch`（自杀监测）、`bipolar`（双相情感障碍）、`depression`（抑郁）以及`off_my_chest`（倾诉）。 训练集共包含43529条样本，测试集共包含10883条样本。 #### 临床文档记录赛道 该数据集的每一行包含11个字段： 1. `dataset`：描述该行数据来源的元数据字段 2. `encounter_id`：每个患者就诊记录的唯一标识符 3. `dialogue`：就诊对话的音频转录文本 4. `note`：就诊结束后生成的临床笔记文本 5. `doctor_name`：接诊医师姓名 6. `patient_gender`：患者性别 7. `patient_age`：患者年龄（单位：岁） 8. `patient_firstname`：患者名 9. `patient_lastname`：患者姓 10. `chief_complaint`：患者主诉 11. `addition_complaints`：患者附加主诉 训练集共包含67条样本，测试集共包含20条样本。 #### 医学教育赛道 该数据集的每一行包含5个字段： 1. `question`：美国医师执照考试（USMLE）试题 2. `answer`：标准答案文本 3. `options`：将每个选择题选项映射至对应答案文本的字典 4. `meta_info`：试题所属考试阶段（Step 1、Step 2及/或Step 3） 5. `answer_idx`：正确选择题选项的索引 训练集共包含10178条样本，测试集共包含1273条样本。 ### 数据集卡片联系人 如有任何疑问或建议，请联系[Michael Yao](mailto:[e-mail])。