| Title: |
Machine learning to predict risk for community-onset Staphylococcus aureus infections in children living in southeastern United States |
| Authors: |
Lin, Xiting; Geng, Ruijin; Menke, Kurt; Edelson, Mike; Yan, Fengxia; Leong, Traci; Rust, George S.; Waller, Lance A.; Johnson, Erica L.; Cheng Immergluck, Lilly |
| Contributors: |
Klein, Eili Y.; U.S. National Library of Medicine; Agency for Healthcare Research and Quality; National Center for Advancing Translational Sciences |
| Source: |
PLOS ONE ; volume 18, issue 9, page e0290375 ; ISSN 1932-6203 |
| Publisher Information: |
Public Library of Science (PLoS) |
| Publication Year: |
2023 |
| Collection: |
PLOS Publications (via CrossRef) |
| Description: |
Staphylococcus aureus ( S . aureus ) is known to cause human infections and since the late 1990s, community-onset antibiotic resistant infections (methicillin resistant S . aureus (MRSA)) continue to cause significant infections in the United States. Skin and soft tissue infections (SSTIs) still account for the majority of these in the outpatient setting. Machine learning can predict the location-based risks for community-level S . aureus infections. Multi-year (2002–2016) electronic health records of children |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1371/journal.pone.0290375 |
| Availability: |
https://doi.org/10.1371/journal.pone.0290375; https://dx.plos.org/10.1371/journal.pone.0290375 |
| Rights: |
http://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.3FAD084F |
| Database: |
BASE |