新生儿遗传代谢病筛查机器学习数据集（表1）
包含30多万条新生儿遗传代谢病筛查记录及机器学习预测结果的数据集，用于评估疾病筛查模型。
基本信息
资源简介
新生儿遗传代谢病筛查机器学习数据集（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 685.89 KB，主题涵盖 machine learning、neonatal screening、inherited metabolic diseases、diagnostic efficiency、carrier，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《Table1_The significance of machine learning in neonatal screening for inherited metabolic diseases.docx》。配套论文《Expanded newborn screening for inherited metabolic disorders by tandem mass spectrometry in a northern Chinese population》发表于 Frontiers in Genetics（2022），DOI：10.3389/fgene.2022.801447。源站首次发布：2024-03-20。
【数据内容】
源站原始描述：BackgroundNeonatal screening for inherited metabolic diseases (IMDs) has been revolutionized by tandem mass spectrometry (MS/MS). This study aimed to enhance neonatal screening for IMDs using machine learning (ML) techniques. MethodsThe study involved the analysis of a comprehensive dataset comprising 309,102 neonatal screening records collected in the Ningbo region, China. An advanced ML system model, encompassing nine distinct algorithms, was employed for the purpose of predicting the presence…
【数据结构与技术规格】
文件数 1 个，合计 685.89 KB。Table1_The significance of machine learning in neonatal screening for inherited metabolic diseases.docx：application/vnd.openxmlformats-officedocument.wordprocessingml.document，685.89 KB。数据集 DOI：10.3389/fped.2024.1366891.s001。
【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：machine learning、neonatal screening、inherited metabolic diseases、diagnostic efficiency、carrier。
【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/Table1_The_significance_of_machine_learning_in_neonatal_screening_for_inherited_metabolic_diseases_docx/25440688
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Yang, Xiangchun; Ding, Shuxia; Zhang, Jianping; Hu, Zhuojie; Zhuang, Danyan; Wang, Fei; et al. (2024). Table1_The significance of machine learning in neonatal screening for inherited metabolic diseases.docx. Frontiers. Dataset
下载信息
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公开数据集受托下载与技术交付服务。
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暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取（Figshare）
- 源站标题：Table1_The significance of machine learning in neonatal screening for inherited metabolic diseases.docx
- DOI：10.3389/fped.2024.1366891.s001
- 发布日期：2024-03-20
- 文件数：1 个
- 文件总体积：685.89 KB
- 源站页面：https://frontiersin.figshare.com/articles/dataset/Table1_The_significance_of_machine_learning_in_neonatal_screening_for_inherited_metabolic_diseases_docx/25440688
命令行下载
curl -L -o "Table1_The significance of machine learning in neonatal screening for inherited metabolic diseases.docx" "https://ndownloader.figshare.com/files/45145828"
文件清单
Table1_The significance of machine learning in neonatal screening for inherited metabolic diseases.docx（685.89 KB）
文件总体积 685.89 KB（源站 API 实测）。
数据说明
BackgroundNeonatal screening for inherited metabolic diseases (IMDs) has been revolutionized by tandem mass spectrometry (MS/MS). This study aimed to enhance neonatal screening for IMDs using machine learning (ML) techniques.
MethodsThe study involved the analysis of a comprehensive dataset comprising 309,102 neonatal screening records collected in the Ningbo region, China. An advanced ML system model, encompassing nine distinct algorithms, was employed for the purpose of predicting the presence of 31 different IMDs. The model was compared with traditional cutoff schemes to assess its diagnostic efficacy. Additionally, 180 suspected positive cases underwent further evaluation.
ResultsThe ML system exhibited a significantly reduced positive rate, from 1.17% to 0.33%, compared to cutoff schemes in the initial screening, minimizing unnecessary recalls and associated stress. In suspected positive cases, the ML system identified 142 true positives with high sensitivity (93.42%) and improved specificity (78.57%) compared to the cutoff scheme. While false negatives emerged, particularly in heterozygous carriers, our study revealed the potential of the ML system to detect asymptomatic cases.
ConclusionThis research provides valuable insights into the potential of ML in pediatric medicine for IMD diagnosis through neonatal screening, emphasizing the need for accurate carrier detection and further research in this domain.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.3389/fped.2024.1366891.s001（source: https://frontiersin.figshare.com/articles/dataset/Table1_The_significance_of_machine_learning_in_neonatal_screening_for_inherited_metabolic_diseases_docx/25440688）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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