骨科疾病诊疗数据集

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。
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 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 length of 1, and the loss function set to "hs". The model performance is evaluated via the `classification_report` method, showing excellent results. Parameter updates synchronize the model, labels, and label names through commands, enabling fast and accurate diagnosis of specialized disease types.

Multi-dimensional Diagnosis and Treatment Data for Patient Master Index Construction: The feature vectors of patient data are defined as information such as patient gender, address, family genetic diseases, allergens, etc. The DBSCAN algorithm is employed to cluster patient data points by grouping data points with similar feature vector densities into the same cluster. Each cluster can be regarded as a patient group, serving as the identifier of the master index.

搜集汇总

数据集介绍

特点

骨科疾病诊疗数据集包含20个字段的200000条数据，每月更新，适用于医疗、教学和科研领域，主要用于诊疗模式研究和药物经济学研究。