I-CAH国际先天性肾上腺皮质增生症注册库血压数据
该数据集衍生自国际先天性肾上腺皮质增生症注册库(I-CAH Registry),包含了CAH患者相关的长期血压随访记录与分析指标,常用于研究激素替代治疗对患者心血管系统的长远影响。由University of Sheffield提供,来自GitHub,数据格式为表格,适用于分类任务。

基本信息
资源简介
I-CAH国际先天性肾上腺皮质增生症注册库血压数据
【数据集背景】
本数据集源站标题为《neilxlawrence/I-CAH_Blood_Pressure》。源站首次发布:2024-10-28。
【数据内容】
源站原始描述:Data analysis code for I-CAH Blood Pressure Analysis by Neil Lawrence # I-CAH_Blood_Pressure Data analysis code for I-CAH Blood Pressure Analysis by Neil Lawrence This repository contains code used to analyse data from the International Congenital Adrenal Hyperplasia Registry study 202107_NL Please note, this repository does not contain patient level data. Patient level data may only be obtained by formal data request from the data controller. Please search ''I-CAH Registry'' and apply for patient…
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面:https://github.com/neilxlawrence/I-CAH_Blood_Pressure
【使用注意】
· 源站最后修改于 2025-03-23,引用时请注意版本时效。
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载,适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档,适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务(收技术服务费,非数据销售)
暂未开放当前数据集主要来源为 Kaggle 公开托管,完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取
git clone https://github.com/neilxlawrence/I-CAH_Blood_Pressure.git
curl -L -o repo.zip https://github.com/neilxlawrence/I-CAH_Blood_Pressure/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录(使用方式)
I-CAH_Blood_Pressure
Data analysis code for I-CAH Blood Pressure Analysis by Neil Lawrence
This repository contains code used to analyse data from the International Congenital Adrenal Hyperplasia Registry study 202107_NL
Please note, this repository does not contain patient level data. Patient level data may only be obtained by formal data request from the data controller. Please search ‘‘I-CAH Registry’’ and apply for patient level data directly from the data controller.
Once patient level data is obtained from the data controller, correct the pathway to the extracted csv file within the code
Also copy the folder bp_functions_folder and it’'s contents into your main directory
Create an empty folder ‘‘bp_data_files_to_load’’ within your main directory
Assess the need for other data files within code and insert these wtihin ‘‘Other Data Extractions’’ as necessary, dependent upon the form in which your data is provided by the data controller
The files are then designed to be run in order, and will load previously prepared data files. This allows the code to be run separately, as the total run time is long, allowing for any bespoke adjustments to be targeted within specific files
Queries to n.r.lawrence@sheffield.ac.uk
数据加载示例(表格/文本类)
import pandas as pd, glob, os
files = (glob.glob(os.path.join(path, "**", "*.csv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tsv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.xlsx"), recursive=True))
print("数据文件:", files)
df = pd.read_csv(files[0])
print(df.shape); print(df.columns.tolist()); print(df.head(3))
数据缺失?
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