乳腺癌专病数据集

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。
电子病历文本细项解析方法：首先对于病历文本数据，进行分层解析，将获取到的文本数据按事件流的方式进行拆解，根据需要解析的各项信息，有针对性的选取包含该信息的内容类别事件流，进一步行各项指标的细项结构化解析。

Classification Model for Specialized Disease Diagnosis Names: Establishes a diagnostic database by analyzing medical literature, clinical data and expert knowledge. After preprocessing including word segmentation and dataset 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, demonstrating excellent results. Parameters are updated by synchronizing the model, labels and label names through commands, enabling fast and accurate diagnosis of specialized disease types.
Method for Fine-grained Parsing of Electronic Medical Record Text: Firstly, hierarchical parsing is conducted on medical record text data. The acquired text data is decomposed according to event streams. Based on the specific information items to be parsed, the event streams of content categories containing the target information are selectively selected, followed by fine-grained structured parsing of various indicators.

搜集汇总

数据集介绍

特点

乳腺癌专病数据集包含51万条医疗记录，每月更新，涵盖诊断、药品、检验等详细信息，适用于医疗、教学和科研领域，支持乳腺癌早期预防和诊疗模式研究。数据集通过专病诊断名称分类模型和电子病历文本细项解析方法进行处理，已在天津知识产权交易平台存证。