前列腺癌专病数据集

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: A diagnostic database is established by analyzing medical literature, clinical data, and expert knowledge. After preprocessing including tokenization and 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 a loss function set to "hs". The model performance is evaluated via the `classification_report` method, achieving satisfactory results. Parameter updates are conducted by synchronizing the model, labels, and label names through commands, enabling fast and accurate diagnosis of specialized disease types.

Multi-dimensional Clinical Data-based Patient Master Index Construction: Patient data feature vectors are defined using information such as patient gender, residential address, family genetic disorders, allergens, and other relevant details. The DBSCAN algorithm is applied to cluster patient data points based on the density of their feature vectors, grouping data points with similar densities into the same cluster. Each cluster can be treated as a patient group, which serves as the identifier for the patient master index.