A novel, integrated approach for understanding and investigating Healthcare Associated Infections: A risk factors constellation analysis.
Mariachiara CarestiaMassimo AndreoniErsilia BuonomoFausto CiccacciLuigi De AngelisGerardo De CarolisPatrizia De FilippisDaniele Di GiovanniLeonardo Emberti GialloretiCarla FontanaLuca GuarenteAndrea MagriniMarco MatteiStefania MoramarcoLaura MorcianoClaudia MosconiStefano OrlandoGiuseppe QuintavalleFabio RiccardiViviana SantoroLeonardo PalombiPublished in: PloS one (2023)
The increasing availability of health data stored in EHRs represents a unique opportunity for the accurate identification of any factor that contributes to the diffusion of HAIs and AMR and for the prompt implementation of effective corrective measures. That said, artificial intelligence might be the future of health data analysis because it may allow for the early identification of patients who are more exposed to the risk of HAIs and for a more efficient monitoring of HAI sources and outbreaks. However, challenges concerning codification, integration, and standardization of health data recording and analysis still need to be addressed.
Keyphrases
- healthcare
- data analysis
- artificial intelligence
- big data
- public health
- risk factors
- health information
- mental health
- machine learning
- end stage renal disease
- electronic health record
- chronic kidney disease
- newly diagnosed
- deep learning
- health promotion
- social media
- patient reported outcomes
- risk assessment
- ionic liquid