机器学习能否利用常规临床数据改善心血管风险预测？

数据集获取（Figshare）

• 源站标题：Can machine-learning improve cardiovascular risk prediction using routine clinical data?
• DOI：10.1371/journal.pone.0174944
• 发布日期：2017-04-04
• 文件数：3 个
• 文件总体积：84.98 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Can_machine-learning_improve_cardiovascular_risk_prediction_using_routine_clinical_data_/4815979

命令行下载

curl -L -o "S1 Table.docx" "https://ndownloader.figshare.com/files/7990843"
curl -L -o "S2 Table.docx" "https://ndownloader.figshare.com/files/7990852"
curl -L -o "S1 Text.docx" "https://ndownloader.figshare.com/files/7990873"

文件清单

• S1 Table.docx（17.78 KB）
• S2 Table.docx（13.08 KB）
• S1 Text.docx（54.12 KB）

文件总体积 84.98 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Background
Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction.
Methods
Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins).
Findings
24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm.
Conclusions
Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0174944（source: https://plos.figshare.com/articles/dataset/Can_machine-learning_improve_cardiovascular_risk_prediction_using_routine_clinical_data_/4815979）。