地球资源数据云——数据资源详情
患者治疗分类
该数据集《Patient Treatment Classification》主要用于二分类任务，数据形态以文本为主，应用场景偏向医疗健康。 题目说明：Electronic Health Record Dataset 任务类型：文本二分类。 建议流程：先做文本清洗与分词，再比较 TF - IDF+线性模型 与 预训练语言模型。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：data - ori.csv。 Context The dataset is Electronic Health Record Predicting collected from a private Hospital in Indonesia. It contains the patients laboratory test results used to determine next patient treatment whether in care or out care patient. The task embedded to the dataset is classification prediction. Content Attribute Information: Given is the attribute name, attribute type, the measurement unit and a brief description. The number of rings is the value to predict: either as a continuous value or as a classification problem.
摘要概览
该数据集《Patient Treatment Classification》主要用于二分类任务，数据形态以文本为主，应用场景偏向医疗健康。 题目说明：Electronic Health Record Dataset
任务类型：文本二分类。
建议流程：先做文本清洗与分词，再比较 TF - IDF+线性模型 与 预训练语言模型。
评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。
可用文件：data - ori.csv。
Context
The dataset is Electronic Health Record Predicting collected from a private Hospital in Indonesia. It contains the patients laboratory test results used to determine next patient treatment whether in care or out care patient. The task embedded to the dataset is classification prediction.
Content
Attribute Information:
Given is the attribute name, attribute type, the measurement unit and a brief description. The number of rings is the value to predict: either as a continuous value or as a classification problem.