Publications
Classification and Personalized Prognostic Assessment on the Basis of Clinical and Genomic Features in Myelodysplastic Syndromes
Abstract Purpose Recurrently mutated genes and chromosomal abnormalities have been identified in myelodysplastic syndromes (MDS). We aim to integrate these genomic features into disease classification and prognostication. Methods We retrospectively enrolled 2,043 patients. Using Bayesian networks and Dirichlet processes, we combined mutations in 47 genes with cytogenetic abnormalities to identify genetic associations and subgroups. Random-effects […]
Connecting the use of innovative treatments and glucocorticoids with the multidisciplinary evaluation through rule-based natural-language processing: a real-world study on patients with rheumatoid arthritis, psoriatic arthritis, and psoriasis
Abstract Background: The impact of a multidisciplinary management of rheumatoid arthritis (RA), psoriatic arthritis (PsA), and psoriasis on systemic glucocorticoids or innovative treatments remains unknown. Rule-based natural language processing and text extraction help to manage large datasets of unstructured information and provide insights into the profile of treatment choices. Methods: We obtained structured information from […]
Artificial intelligence processing electronic health records to identify commonalities and comorbidities cluster at Immuno Center Humanitas
Abstract Background Comorbidities are common in chronic inflammatory conditions, requiring multidisciplinary treatment approach. Understanding the link between a single disease and its comorbidities is important for appropriate treatment and management. We evaluate the ability of an NLP-based process for knowledge discovery to detect information about pathologies, patients’ phenotype, doctors’ prescriptions and commonalities in electronic medical […]
Machine Learning Prediction Model to Predict Length of Stay of Patients Undergoing Hip or Knee Arthroplasties: Results from a High-Volume Single-Center Multivariate Analysis
Abstract The growth of arthroplasty procedures requires innovative strategies to reduce inpatients’ hospital length of stay (LOS). This study aims to develop a machine learning prediction model that may aid in predicting LOS after hip or knee arthroplasties. Methods: A collection of all the clinical notes of patients who underwent elective primary or revision arthroplasty […]
Artificial intelligence in paediatric cancer: Insights from innovation experts in the UNICA4EU project
Abstract This article presents the results of interviews with AI development and innovation experts, focusing on the potential contribution of AI to paediatric cancer treatment, its barriers and facilitators. AI-based technologies are expanding in health care provision and potentially to paediatric oncology, particular imageology. However, no AI based technology specifically developed for paediatric cancer has […]
Exploring Zero-Shot Anomaly Detection with CLIP in Medical Imaging: Are We There Yet?
Abstract Zero-shot anomaly detection (ZSAD) offers potential for identifying anomalies in medical imaging without task-specific training. In this paper, we evaluate CLIP-based models, originally developed for industrial tasks, on brain tumor detection using the BraTS-MET dataset. Our analysis examines their ability to detect medical-specific anomalies with no or minimal supervision, addressing the challenges posed by […]