使用机器学习模型预测危重儿童通气支持调整后氧饱和度：一项单中心预试验研究

数据集获取（Figshare）

• 源站标题：Using machine learning models to predict oxygen saturation following ventilator support adjustment in critically ill children: A single center pilot study
• DOI：10.1371/journal.pone.0198921
• 发布日期：2019-02-20
• 文件数：2 个
• 文件总体积：216.26 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Using_machine_learning_models_to_predict_oxygen_saturation_following_ventilator_support_adjustment_in_critically_ill_children_A_single_center_pilot_study/7749032

命令行下载

curl -L -o "pone.0198921.s001.docx" "https://ndownloader.figshare.com/files/14417660"
curl -L -o "pone.0198921.s002.docx" "https://ndownloader.figshare.com/files/14417663"

文件清单

• pone.0198921.s001.docx（109.68 KB）
• pone.0198921.s002.docx（106.58 KB）

文件总体积 216.26 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
In an intensive care units, experts in mechanical ventilation are not continuously at patient’s bedside to adjust ventilation settings and to analyze the impact of these adjustments on gas exchange. The development of clinical decision support systems analyzing patients’ data in real time offers an opportunity to fill this gap.
Objective
The objective of this study was to determine whether a machine learning predictive model could be trained on a set of clinical data and used to predict transcutaneous hemoglobin oxygen saturation 5 min (5min SpO2) after a ventilator setting change.
Data sources
Data of mechanically ventilated children admitted between May 2015 and April 2017 were included and extracted from a high-resolution research database. More than 776,727 data rows were obtained from 610 patients, discretized into 3 class labels (Performance metrics of predictive models
Due to data imbalance, four different data balancing processes were applied. Then, two machine learning models (artificial neural network and Bootstrap aggregation of complex decision trees) were trained and tested on these four different balanced datasets. The best model predicted SpO2 with area under the curves Conclusion
This single center pilot study using machine learning predictive model resulted in an algorithm with poor accuracy. The comparison of machine learning models showed that bagged complex trees was a promising approach. However, there is a need to improve these models before incorporating them into a clinical decision support systems. One potentially solution for improving predictive model, would be to increase the amount of data available to limit over-fitting that is potentially one of the cause for poor classification performances for 2 of the three class labels.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0198921（source: https://plos.figshare.com/articles/dataset/Using_machine_learning_models_to_predict_oxygen_saturation_following_ventilator_support_adjustment_in_critically_ill_children_A_single_center_pilot_study/7749032）。