心脑血管疾病诊疗数据集

专病诊断名称分类模型：通过分析医学文献、临床数据和专家知识，建立一个诊断数据库。经过分词和打乱顺序的预处理后，使用 train_supervised 函数进行训练（迭代200次，学习率0.1，词N-grams长度为1，损失函数为"hs"）。模型性能通过 classification_report 方法评估，表现良好。参数更新通过命令同步模型、标签和标签名，从而快速、准确地诊断专病类型。
专病治疗方案分类模型：该模型通过分析大量临床数据和医学文献，识别并分类与特定专病相关的治疗方案。数据集包括13个类别，样本不平衡问题通过裁剪和复制补充解决。训练集和测试集按8:2比例划分。使用 GloVe对原始数据进行预处理。模型训练使用CNN构建卷积层、池化层和全连接层。模型调优后，判断准确率、召回率选择最佳参数组合。参数更新通过指定命令完成，确保模型、标签和标签名同步。

Specific Disease Diagnosis Name Classification Model: A diagnostic database is constructed by analyzing medical literature, clinical data and expert knowledge. After preprocessing including word segmentation and data shuffling, the model is trained using the `train_supervised` function with 200 training iterations, a learning rate of 0.1, word N-grams of length 1, and the loss function set to "hs". The model's performance is evaluated via the `classification_report` method, achieving satisfactory results. Parameter updates are performed via commands that synchronize the model, labels and label names, enabling fast and accurate diagnosis of specific disease types.

Specific Disease Treatment Plan Classification Model: This model identifies and classifies treatment plans related to specific diseases by analyzing large volumes of clinical data and medical literature. The dataset contains 13 categories, and the class imbalance issue is addressed through data cropping and replication-based augmentation. The dataset is split into training and test sets at an 8:2 ratio. Raw data is preprocessed using GloVe embeddings. The model is built with a CNN architecture consisting of convolutional layers, pooling layers and fully connected layers. After model tuning, the optimal parameter combination is selected based on accuracy and recall metrics. Parameter updates are completed via designated commands to ensure synchronization of the model, labels and label names.

搜集汇总

数据集介绍

特点

心脑血管疾病诊疗数据集包含120万条诊疗记录，涵盖20个字段，每月更新，适用于医疗、教学和科研领域的研究。