冈比亚肺炎儿童死亡率预测神经网络模型数据表

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Deploying Machine Learning Models Using Progressive Web Applications: Implementation Using a Neural Network Prediction Model for Pneumonia Related Child Mortality in The Gambia.pdf
• DOI：10.3389/fpubh.2021.772620.s001
• 发布日期：2022-02-18
• 文件数：1 个
• 文件总体积：579.77 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Deploying_Machine_Learning_Models_Using_Progressive_Web_Applications_Implementation_Using_a_Neural_Network_Prediction_Model_for_Pneumonia_Related_Child_Mortality_in_The_Gambia_pdf/19194044

命令行下载

curl -L -o "Data_Sheet_1_Deploying Machine Learning Models Using Progressive Web Applications: Implementation Using a Neural Network Prediction Model for Pneumoni.pdf" "https://ndownloader.figshare.com/files/34102262"

文件清单

• Data_Sheet_1_Deploying Machine Learning Models Using Progressive Web Applications: Implementation Using a Neural Network Prediction Model for Pneumoni.pdf（579.77 KB）

文件总体积 579.77 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

BackgroundTranslating research outputs into practical tools for medical practitioners is a neglected area and could have a substantial impact. One of the barriers to implementing artificial intelligence (AI) and machine learning (ML) applications is their practical deployment in the field. Traditional web-based (i.e., server sided) applications are dependent on reliable internet connections, which may not be readily available in rural areas. Native mobile apps require device specific programming skills as well as contemporary hardware and software, with often rapid and unpredictable platform specific changes. This is a major challenge for using AI/ML tools in resource-limited settings.
MethodsAn emerging technology, progressive web applications (PWAs), first introduced by Google in 2015, offers an opportunity to overcome the challenges of deploying bespoke AI/ML systems. The same PWA code can be implemented across all desktop platforms, iOS and Android phones and tablets. In addition to platform independence, a PWA can be designed to be primarily offline.
ResultsWe demonstrate how a neural network-based pneumonia mortality prediction triage tool was migrated from a typical academic framework (paper and web-based prototype) to a tool that can be used offline on any mobile phone—the most convenient deployment vehicle. After an initial online connection to download the software, the application runs entirely offline, reading data from cached memory, and running code via JavaScript. On mobile devices the application is installed as a native app, without the inconvenience of platform specific code through manufacturer code stores.
DiscussionWe show that an ML application can be deployed as a platform independent offline PWA using a pneumonia-related child mortality prediction tool as an example. The aim of this tool was to assist clinical staff in triaging children for hospital admission, by predicting their risk of death. PWAs function seamlessly when their host devices lose internet connectivity, making them ideal for e-health apps that can help improve health and save lives in resource-limited settings in line with the UN Sustainable Development Goal 3 (SDG3).

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fpubh.2021.772620.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Deploying_Machine_Learning_Models_Using_Progressive_Web_Applications_Implementation_Using_a_Neural_Network_Prediction_Model_for_Pneumonia_Related_Child_Mortality_in_The_Gambia_pdf/19194044）。