Predicting the primary infection source of Escherichia coli bacteremia using virulence-associated genes.
Christian Schaadt IlsbyFrederik Boetius HertzHenrik WesthJonathan MonkPeder WorningHelle Krogh JohansenKatrine Hartung HansenMette PinholtPublished in: European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology (2024)
WGS data was used to predict the primary source of E. coli bacteremia and is an attempt on a new and different type of infection source identification. Genomic data showed potential to be utilized to predict the primary source of infection; however, discrepancy between the best performing profile of VAGs between acute care hospitals and tertiary hospitals makes it difficult to implement in clinical practice.
Keyphrases
- escherichia coli
- acute care
- clinical practice
- healthcare
- electronic health record
- big data
- staphylococcus aureus
- bioinformatics analysis
- machine learning
- genome wide
- gene expression
- cystic fibrosis
- deep learning
- risk assessment
- data analysis
- artificial intelligence
- transcription factor
- multidrug resistant
- climate change
- genome wide analysis