基于人工神经网络的新生儿败血症诊断决策表1
基于25项母体和新生儿临床特征的表格数据集，用于人工神经网络模型训练，辅助新生儿败血症诊断。
基本信息
资源简介
基于人工神经网络的新生儿败血症诊断决策表1
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集，收录 1 个文件、合计 718.01 KB，主题涵盖 newborn、neural network、sepsis、machine learning、prematurity，可用于统计建模、特征工程与队列分析，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Table_1_Neonatal Sepsis Diagnosis Decision-Making Based on Artificial Neural Networks.docx》。配套论文《Neonatal sepsis: Highlighting the principles of diagnosis and management》发表于 South African Journal of Child Health（2017），DOI：10.7196/sajch.2017.v11i2.1244。源站首次发布：2020-09-11。
【数据内容】
源站原始描述：Neonatal sepsis remains difficult to diagnose due to its non-specific signs and symptoms. Traditional scoring systems help to discriminate between septic or not patients, but they do not consider every single patient particularity. Thus, the purpose of this study was to develop an early- and late-onset neonatal sepsis diagnosis model, based on clinical maternal and neonatal data from electronic records, at the time of clinical suspicion. A predictive model was obtained by training and validating…
【数据结构与技术规格】
文件数 1 个，合计 718.01 KB。Table_1_Neonatal Sepsis Diagnosis Decision-Making Based on Artificial Neural Networks.docx：application/vnd.openxmlformats-officedocument.wordprocessingml.document，718.01 KB。数据集 DOI：10.3389/fped.2020.00525.s002。
【主题与分类】
源站学科分类：Foetal Development and Medicine、Obstetrics and Gynaecology、Paediatrics、Paediatrics and Reproductive Medicine not elsewhere classified。主题标签：newborn、neural network、sepsis、machine learning、prematurity。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Neonatal_Sepsis_Diagnosis_Decision-Making_Based_on_Artificial_Neural_Networks_docx/12942857
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Helguera-Repetto, Addy Cecilia; Soto-Ramírez, María Dolores; Villavicencio-Carrisoza, Oscar; Yong-Mendoza, Samantha; Yong-Mendoza, Angélica; León-Juárez, Moisés; et al. (2020). Table_1_Neonatal Sepsis Diagnosis Decision-Making Based on Artificial Neural Networks.docx. Frontiers. Dataset
【使用注意】
· 源站最后修改于 2023-06-03，引用时请注意版本时效。
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：Table_1_Neonatal Sepsis Diagnosis Decision-Making Based on Artificial Neural Networks.docx
- DOI：10.3389/fped.2020.00525.s002
- 发布日期：2020-09-11
- 文件数：1 个
- 文件总体积：718.01 KB
- 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Neonatal_Sepsis_Diagnosis_Decision-Making_Based_on_Artificial_Neural_Networks_docx/12942857
命令行下载
curl -L -o "Table_1_Neonatal Sepsis Diagnosis Decision-Making Based on Artificial Neural Networks.docx" "https://ndownloader.figshare.com/files/24647705"
文件清单
Table_1_Neonatal Sepsis Diagnosis Decision-Making Based on Artificial Neural Networks.docx（718.01 KB）
文件总体积 718.01 KB（源站 API 实测）。
数据说明
Neonatal sepsis remains difficult to diagnose due to its non-specific signs and symptoms. Traditional scoring systems help to discriminate between septic or not patients, but they do not consider every single patient particularity. Thus, the purpose of this study was to develop an early- and late-onset neonatal sepsis diagnosis model, based on clinical maternal and neonatal data from electronic records, at the time of clinical suspicion. A predictive model was obtained by training and validating an artificial Neural Networks (ANN) algorithm with a balanced dataset consisting of preterm and term non-septic or septic neonates (early- and late-onset), with negative and positive culture results, respectively, using 25 maternal and neonatal features. The outcome of the model was sepsis or not. The performance measures of the model, evaluated with an independent dataset, outperformed physician’'s diagnosis using the same features based on traditional scoring systems, with a 93.3% sensitivity, an 80.0% specificity, a 94.4% AUROC, and a regression coefficient of 0.974 between actual and simulated results. The model also performed well-relative to the state-of-the-art methods using similar maternal/neonatal variables. The top 10 factors estimating sepsis were maternal age, cervicovaginitis and neonatal: fever, apneas, platelet counts, gender, bradypnea, band cells, catheter use, and birth weight.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.3389/fped.2020.00525.s002（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Neonatal_Sepsis_Diagnosis_Decision-Making_Based_on_Artificial_Neural_Networks_docx/12942857）。
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