基于机器学习贝叶斯网络的美国儿童保育设施水铅测试决策改进
包含美国北卡罗来纳州4000多个儿童保育设施的2.2万多个水龙头铅浓度及设施特征，用于贝叶斯网络模型预测水铅风险以优化测试资源。
基本信息
资源简介
基于机器学习贝叶斯网络的美国儿童保育设施水铅测试决策改进
【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 1.52 MB，主题涵盖 single tap exceeding、optimize limited resources、north carolina according、learned bayesian network、improved decision making，可用于统计建模、特征工程与队列分析，CC BY-NC 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Improved Decision
Making for Water Lead Testing in
U.S. Child Care Facilities Using Machine-Learned Bayesian Networks》。配套论文《Improved Decision Making for Water Lead Testing in U.S. Child Care Facilities Using Machine-Learned Bayesian Networks》发表于 Environmental Science & Technology（2023），DOI：10.1021/acs.est.2c07477。源站首次发布：2023-03-18。
【数据内容】
源站原始描述：Tap water lead testing programs in the U.S. need improved methods for identifying high-risk facilities to optimize limited resources. In this study, machine-learned Bayesian network (BN) models were used to predict building-wide water lead risk in over 4,000 child care facilities in North Carolina according to maximum and 90th percentile lead levels from water lead concentrations at 22,943 taps. The performance of the BN models was compared to common alternative risk factors, or heuristics, used…
【数据结构与技术规格】
文件数 1 个，合计 1.52 MB。es2c07477_si_002.zip：application/zip，1.52 MB。数据集 DOI：10.1021/acs.est.2c07477.s002。
【主题与分类】
源站学科分类：Medicine、Environmental Sciences not elsewhere classified、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Mathematical Sciences not elsewhere classified、Cancer。主题标签：single tap exceeding、optimize limited resources、north carolina according、learned bayesian network、improved decision making、wide water lead、water lead testing、water lead concentrations、need improved methods、using bn model。
【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://acs.figshare.com/articles/dataset/Improved_Decision_Making_for_Water_Lead_Testing_in_U_S_Child_Care_Facilities_Using_Machine-Learned_Bayesian_Networks/22298643
使用许可：CC BY-NC 4.0（https://creativecommons.org/licenses/by-nc/4.0/）。
规范引用：Mulhern, Riley E.; Kondash, AJ; Norman, Ed; Johnson, Joseph; Levine, Keith; McWilliams, Andrea; et al. (2023). Improved Decision Making for Water Lead Testing in U.S. Child Care Facilities Using Machine-Learned Bayesian Networks. ACS Publications. Dataset
【使用注意】
· 源站最后修改于 2023-05-31，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：Improved Decision
Making for Water Lead Testing in
U.S. Child Care Facilities Using Machine-Learned Bayesian Networks - DOI：10.1021/acs.est.2c07477.s002
- 发布日期：2023-03-18
- 文件数：1 个
- 文件总体积：1.52 MB
- 源站页面：https://acs.figshare.com/articles/dataset/Improved_Decision_Making_for_Water_Lead_Testing_in_U_S_Child_Care_Facilities_Using_Machine-Learned_Bayesian_Networks/22298643
命令行下载
curl -L -o "es2c07477_si_002.zip" "https://ndownloader.figshare.com/files/39661239"
文件清单
es2c07477_si_002.zip（1.52 MB）
文件总体积 1.52 MB（源站 API 实测）。
数据说明
Tap water lead testing programs in the U.S. need improved
methods
for identifying high-risk facilities to optimize limited resources.
In this study, machine-learned Bayesian network (BN) models were used
to predict building-wide water lead risk in over 4,000 child care
facilities in North Carolina according to maximum and 90th percentile
lead levels from water lead concentrations at 22,943 taps. The performance
of the BN models was compared to common alternative risk factors,
or heuristics, used to inform water lead testing programs among child
care facilities including building age, water source, and Head Start
program status. The BN models identified a range of variables associated
with building-wide water lead, with facilities that serve low-income
families, rely on groundwater, and have more taps exhibiting greater
risk. Models predicting the probability of a single tap exceeding
each target concentration performed better than models predicting
facilities with clustered high-risk taps. The BN models’ Fβ-scores outperformed each of the alternative heuristics
by 118–213%. This represents up to a 60% increase in the number
of high-risk facilities that could be identified and up to a 49% decrease
in the number of samples that would need to be collected by using
BN model-informed sampling compared to using simple heuristics. Overall,
this study demonstrates the value of machine-learning approaches for
identifying high water lead risk that could improve lead testing programs
nationwide.
许可
CC BY-NC 4.0
引用
学术使用请引用 DOI 10.1021/acs.est.2c07477.s002（source: https://acs.figshare.com/articles/dataset/Improved_Decision_Making_for_Water_Lead_Testing_in_U_S_Child_Care_Facilities_Using_Machine-Learned_Bayesian_Networks/22298643）。
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