MELD-Plus：一个可泛化的肝硬化预测风险评分

数据集获取（Figshare）

• 源站标题：The MELD-Plus: A generalizable prediction risk score in cirrhosis
• DOI：10.1371/journal.pone.0186301
• 发布日期：2017-10-25
• 文件数：4 个
• 文件总体积：152.93 KB
• 源站页面：https://plos.figshare.com/articles/dataset/The_MELD-Plus_A_generalizable_prediction_risk_score_in_cirrhosis/5538616

命令行下载

curl -L -o "S1 Table.docx" "https://ndownloader.figshare.com/files/9590626"
curl -L -o "S2 Table.docx" "https://ndownloader.figshare.com/files/9590644"
curl -L -o "S3 Table.docx" "https://ndownloader.figshare.com/files/9590662"
curl -L -o "S4 Table.docx" "https://ndownloader.figshare.com/files/9590680"

文件清单

• S1 Table.docx（35.51 KB）
• S2 Table.docx（34.57 KB）
• S3 Table.docx（41.68 KB）
• S4 Table.docx（41.17 KB）

文件总体积 152.93 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 and aims
Accurate assessment of the risk of mortality following a cirrhosis-related admission can enable health-care providers to identify high-risk patients and modify treatment plans to decrease the risk of mortality.
Methods
We developed a post-discharge mortality prediction model for patients with a cirrhosis-related admission using a population of 314,292 patients who received care either at Massachusetts General Hospital (MGH) or Brigham and Women’s Hospital (BWH) between 1992 and 2010. We extracted 68 variables from the electronic medical records (EMRs), including demographics, laboratory values, diagnosis codes, and medications. We then used a regularized logistic regression to select the most informative variables and created a risk score that comprises the selected variables. To evaluate the potential for generalizability of our score, we applied it on all cirrhosis-related admissions between 2010 and 2015 at an independent EMR data source of more than 18 million patients, pooled from different health-care systems with EMRs. We calculated the areas under the receiver operating characteristic curves (AUROCs) to assess prediction performance.
Results
We identified 4,781 cirrhosis-related admissions at MGH/BWH hospitals, of which 778 resulted in death within 90 days of discharge. Nine variables were the most effective predictors for 90-day mortality, and these included all MELD-Na’s components, as well as albumin, total cholesterol, white blood cell count, age, and length of stay. Applying our nine-variable risk score (denoted as “MELD-Plus”) resulted in an improvement over MELD and MELD-Na scores in several prediction models. On the MGH/BWH 90-day model, MELD-Plus improved the performance of MELD-Na by 11.4% (0.78 [95% CI, 0.75–0.81] versus 0.70 [95% CI, 0.66–0.73]). In the MGH/BWH approximate 1-year model, MELD-Plus improved the performance of MELD-Na by 8.3% (0.78 [95% CI, 0.76–0.79] versus 0.72 [95% CI, 0.71–0.73]). Performance improvement was similar when the novel MELD-Plus risk score was applied to an independent database; when considering 24,042 cirrhosis-related admissions, MELD-Plus improved the performance of MELD-Na by 16.9% (0.69 [

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0186301（source: https://plos.figshare.com/articles/dataset/The_MELD-Plus_A_generalizable_prediction_risk_score_in_cirrhosis/5538616）。