Insight Extraction From E-Health Bookings by Means of Hypergraph and Machine Learning.

New technologies are transforming medicine, and this revolution starts with data. Usually, health services within public healthcare systems are accessed through a booking centre managed by local health authorities and controlled by the regional government. In this perspective, structuring e-health data through a Knowledge Graph (KG) approach can provide a feasible method to quickly and simply organize data and/or retrieve new information.

Predictive Medicine for Salivary Gland Tumours Identification Through Deep Learning.

Nowadays, predictive medicine begins to become a reality thanks to Artificial Intelligence (AI) which allows, through the processing of huge amounts of data, to identify correlations not perceptible to the human brain. The application of AI in predictive diagnostics is increasingly pervasive; through the use and interpretation of data, the first signs of some diseases (i.e.

Precision medicine and machine learning towards the prediction of the outcome of potential celiac disease.

Potential Celiac Patients (PCD) bear the Celiac Disease (CD) genetic predisposition, a significant production of antihuman transglutaminase antibodies, but no morphological changes in the small bowel mucosa. A minority of patients (17%) showed clinical symptoms and need a gluten free diet at time of diagnosis, while the majority progress over several years (up to a decade) without any clinical problem neither a progression of the small intestine mucosal damage even when they continued to assume gluten in their diet. Recently we developed a traditional multivariate approach to predict the natural history, on the base of the information at enrolment (time 0) by a discriminant analysis model.

A deep learning approach for facility patient attendance prediction based on medical booking data.

Nowadays, data-driven methodologies based on the clinical history of patients represent a promising research field in which personalized and intelligent healthcare systems can be opportunely designed and developed. In this perspective, Machine Learning (ML) algorithms can be efficiently adopted to deploy smart services to enhance the overall quality of healthcare systems. In this work, starting from an in-depth analysis of a data set composed of millions of medical booking records collected from the public healthcare organization in the region of Campania, Italy, we have developed a predictive model to extract useful knowledge on patients, medical staff, and related healthcare structures.