Improving management of febrile neutropenia in oncology patients: the role of artificial intelligence and machine learning.
Antonio Gallardo-PizarroOlivier PeyronyMariana ChumbitaPatricia Monzo-GalloTommaso Francesco AielloChristian Teijon-LumbrerasEmmanuelle GrasJosep MensaAlex SorianoCarolina Garcia-VidalPublished in: Expert review of anti-infective therapy (2024)
There is promising potential of implementing accurate AI and ML models whilst managing FN. However, their integration as viable clinical tools poses challenges, including technical and implementation barriers. Improving global accessibility, fostering interdisciplinary collaboration, and addressing ethical and security considerations are essential. By overcoming these challenges, we could transform personalized care for patients with FN.
Keyphrases
- artificial intelligence
- machine learning
- big data
- deep learning
- quality improvement
- healthcare
- palliative care
- end stage renal disease
- ejection fraction
- newly diagnosed
- chronic kidney disease
- primary care
- prognostic factors
- high resolution
- risk assessment
- patient reported outcomes
- public health
- global health
- chronic pain
- human health
- decision making
- health insurance