首页|Johns Hopkins University Bloomberg School of Public Health Researcher Reports Re search in Machine Learning (Estimated Childhood Lead Exposure From Drinking Wate r in Chicago)

Johns Hopkins University Bloomberg School of Public Health Researcher Reports Re search in Machine Learning (Estimated Childhood Lead Exposure From Drinking Wate r in Chicago)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on artificial intelligence is now available. According to news reporting from Baltimore, Maryland, by News Rx journalists, research stated, "Importance: There is no level of lead in drink ing water considered to be safe, yet lead service lines are still commonly used in water systems across the US. To identify the extent of lead-contaminated drin king water in Chicago, Illinois, and model its impact on children younger than 6 years." Our news editors obtained a quote from the research from Johns Hopkins Universit y Bloomberg School of Public Health: "Design, Setting, and Participants: For thi s cross-sectional study, a retrospective assessment was performed of lead exposu re based on household tests collected from January 2016 to September 2023. Tests were obtained from households in Chicago that registered for a free self-admini stered testing service for lead exposure. Machine learning and microsimulation w ere used to estimate citywide childhood lead exposure. Exposure: Lead-contaminat ed drinking water, measured in parts per billion. Main Outcomes and Measures: Nu mber of children younger than 6 years exposed to leadcontaminated water. A tota l of 38 385 household lead tests were collected. An estimated 68% (95% uncertainty interval, 66%-69%) of c hildren younger than 6 years were exposed to lead-contaminated water, correspond ing to 129 000 children (95% uncertainty interval, 128 000-131 000 children)."

Johns Hopkins University Bloomberg Schoo l of Public HealthBaltimoreMarylandUnited StatesNorth and Central Americ aCyborgsEmerging TechnologiesMachine Learning

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Apr.2)