肥胖相关疾病数据集

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。
多维诊疗数据构建患者主索引：将患者数据特征向量定义为患者性别、住址、家族遗传病、过敏原等信息，使用DBSCAN算法，基于特征向量的密度，将密度相近的数据点划为同一个簇，将患者数据点进行聚类，每个聚类可以视为一个患者群体，作为主索引的标识

Specialized Disease Diagnosis Name Classification Model: By analyzing medical literature, clinical data and expert knowledge, a diagnostic database is established. 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 good results. Parameter updates are performed by synchronizing the model, labels and label names through commands, enabling fast and accurate diagnosis of specialized disease types.

Patient Master Index Construction via Multi-dimensional Medical and Treatment Data: The patient data feature vector is defined as information including patient gender, residential address, family genetic disorders, allergens and other relevant attributes. The DBSCAN algorithm is utilized to cluster patient data points, where data points with similar feature vector densities are assigned to the same cluster. Each cluster can be regarded as a patient group, serving as the identifier for the patient master index.

搜集汇总

数据集介绍

特点

肥胖相关疾病数据集包含44万条患者数据，涵盖性别、BMI、共病诊断等20个字段，每月更新，适用于医疗、教学和科研领域的研究。