心脏骤停后院内死亡风险预测机器学习研究数据

数据集获取（Figshare）

• 源站标题：Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study
• DOI：10.1371/journal.pmed.1002709
• 发布日期：2018-11-30
• 文件数：5 个
• 文件总体积：9.82 MB
• 源站页面：https://plos.figshare.com/articles/dataset/Characterising_risk_of_in-hospital_mortality_following_cardiac_arrest_using_machine_learning_A_retrospective_international_registry_study/7407017

命令行下载

curl -L -o "pmed.1002709.s001.docx" "https://ndownloader.figshare.com/files/13701284"
curl -L -o "pmed.1002709.s002.png" "https://ndownloader.figshare.com/files/13701287"
curl -L -o "pmed.1002709.s003.docx" "https://ndownloader.figshare.com/files/13701290"
curl -L -o "pmed.1002709.s004.docx" "https://ndownloader.figshare.com/files/13701293"
curl -L -o "pmed.1002709.s005.docx" "https://ndownloader.figshare.com/files/13701296"

文件清单

• pmed.1002709.s001.docx（8.03 MB）
• pmed.1002709.s002.png（1.67 MB）
• pmed.1002709.s003.docx（13.63 KB）
• pmed.1002709.s004.docx（13.75 KB）
• pmed.1002709.s005.docx（90.61 KB）

文件总体积 9.82 MB（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
Resuscitated cardiac arrest is associated with high mortality; however, the ability to estimate risk of adverse outcomes using existing illness severity scores is limited. Using in-hospital data available within the first 24 hours of admission, we aimed to develop more accurate models of risk prediction using both logistic regression (LR) and machine learning (ML) techniques, with a combination of demographic, physiologic, and biochemical information.
Methods and findings
Patient-level data were extracted from the Australian and New Zealand Intensive Care Society (ANZICS) Adult Patient Database for patients who had experienced a cardiac arrest within 24 hours prior to admission to an intensive care unit (ICU) during the period January 2006 to December 2016. The primary outcome was in-hospital mortality. The models were trained and tested on a dataset (split 90:10) including age, lowest and highest physiologic variables during the first 24 hours, and key past medical history. LR and 5 ML approaches (gradient boosting machine [GBM], support vector classifier [SVC], random forest [RF], artificial neural network [ANN], and an ensemble) were compared to the APACHE III and Australian and New Zealand Risk of Death (ANZROD) predictions. In all, 39,566 patients from 186 ICUs were analysed. Mean (±SD) age was 61 ± 17 years; 65% were male. Overall in-hospital mortality was 45.5%. Models were evaluated in the test set. The APACHE III and ANZROD scores demonstrated good discrimination (area under the receiver operating characteristic curve [AUROC] = 0.80 [95% CI 0.79–0.82] and 0.81 [95% CI 0.8–0.82], respectively) and modest calibration (Brier score 0.19 for both), which was slightly improved by LR (AUROC = 0.82 [95% CI 0.81–0.83], DeLong test, p 0.001). Discrimination was significantly improved using ML models (ensemble and GBM AUROCs = 0.87 [95% CI 0.86–0.88], DeLong test, p 0.001), with an improvement in performance (Brier score reduction of 22%). Explainability models were created to assist in identifying the physiologic features that most contributed to an individual patient’s survival. Key limitations include the absence of pre-hospital data and absence of

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pmed.1002709（source: https://plos.figshare.com/articles/dataset/Characterising_risk_of_in-hospital_mortality_following_cardiac_arrest_using_machine_learning_A_retrospective_international_registry_study/7407017）。