Early detection of bloodstream infection in critically ill children using artificial intelligence

We developed a machine learning model that predicts BSI with acceptable performance. Further research is necessary to validate its effectiveness.

Authors

Institutions

Publication Details

Journal
Acute and Critical Care
Published
2024-11-26
DOI
https://doi.org/10.4266/acc.2024.00752
Citations
2
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
0.81

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Early detection of bloodstream infection in critically ill children using artificial intelligence

Hye-ji Han, June Dong Park, Kyunghoon Kim
2 citations
Acute and Critical Care
COVID-19 diagnosis using AI
0.81
article

Early detection of bloodstream infection in critically ill children using artificial intelligence

Hye-ji Han, June Dong Park, Kyunghoon Kim
article en
2 citations

Abstract

We developed a machine learning model that predicts BSI with acceptable performance. Further research is necessary to validate its effectiveness.

Acute and Critical CareVol. 39(4)
Seoul National University (KR), Seoul National University Hospital (KR), Seoul National University Bundang Hospital (KR)
Seoul National University Bundang Hospital, Seoul National University
Openalex Percentile: Top 24%
COVID-19 diagnosis using AI
0.81
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.