0000000000606849
AUTHOR
Rinaldi L
Pollen-induced allergic rhinitis in 1360 Italian children: comorbidities and determinants of severity
BACKGROUND: Pollen-induced allergic rhinoconjunctivitis (AR) is highly prevalent and rapidly evolving during childhood. General practitioners may not be fully aware of the nature and severity of symptoms experienced by patients and might underestimate the prevalence of moderate or severe disease. Thus, the relevance of early diagnosis and intervention may be overlooked. OBJECTIVES: To investigate the severity of pollen-induced AR and its determinants in Italian children referred to allergy specialists and who had never received specific immunotherapy (SIT). METHODS: Children (age 4-18 yr) affected by pollen-induced AR who had never undergone SIT were recruited between May 2009 and June 2011…
Machine learning classification for COVID19 patients performed on small datasets of CT scans.
In this work we evaluated the possibility of carrying out classifications of the outcome of patients with COVID19 disease through machine learning (ML) techniques working on small datasets of computed tomography (CT) images. In fact, one of the most common problems for medical artificial intelligence (AI) applications is the limited availability of annotated clinical data for model training. In the framework of the artificial intelligence in medicine (AIM) project funded by INFN, we analyzed datasets of CT scans of 79 subjects combined with clinical data containing information relating to positive outcome (no need for intensive care) or poor prognosis (admission into intensive care unit and…