Machine learning-based algorithms applied to drug prescriptions and other healthcare services in the Sicilian claims database to identify acromegaly as a model for the earlier diagnosis of rare diseases.

Acromegaly is a rare disease characterized by a diagnostic delay ranging from 5 to 10 years from the symptoms' onset. The aim of this study was to develop and internally validate machine-learning algorithms to identify a combination of variables for the early diagnosis of acromegaly. This retrospective population-based study was conducted between 2011 and 2018 using data from the claims databases of Sicily Region, in Southern Italy.

Discovering predictive temporal patterns for Acute Kidney Injury from critical care data.

Acute Kidney Injury is a severe clinical condition with a high risk of multi-organs complications and mortality. For this reason, early recognition is crucial. Our proposal based on a 3-window framework discovers all hidden regularities, called Approximate Predictive Functional Dependencies, with the aim to enable early recognition of high-risk patients during hospitalization in the Intensive Care Unit (ICU).

A manifesto on explainability for artificial intelligence in medicine.

The rapid increase of interest in, and use of, artificial intelligence (AI) in computer applications has raised a parallel concern about its ability (or lack thereof) to provide understandable, or explainable, output to users. This concern is especially legitimate in biomedical contexts, where patient safety is of paramount importance. This position paper brings together seven researchers working in the field with different roles and perspectives, to explore in depth the concept of explainable AI, or XAI, offering a functional definition and conceptual framework or model that can be used when considering XAI.

Latent Class Trajectory Modeling of 2-Component Disease Activity Score in 28 Joints Identifies Multiple Rheumatoid Arthritis Phenotypes of Response to Biologic Disease-Modifying Antirheumatic Drugs.

Objective: To determine whether using a reweighted disease activity score that better reflects joint synovitis, i.e., the 2-component Disease Activity Score in 28 joints (DAS28) (based on swollen joint count and C-reactive protein level), produces more clinically relevant treatment outcome trajectories compared to the standard 4-component DAS28.

A Dashboard for Latent Class Trajectory Modeling: Application in Rheumatoid Arthritis.

A key trend in current medical research is a shift from a one-size-fit-all to precision treatment strategies, where the focus is on identifying narrow subgroups of the population that would benefit from a given intervention. Precision medicine will greatly benefit from accessible tools that clinicians can use to identify such subgroups, and to generate novel inferences about the patient population they are treating. We present a novel dashboard app that enables clinician users to explore patient subgroups with varying longitudinal treatment response, using latent class mixed modeling.

Protocol of a scoping review assessing injury rates and their determinants among healthcare workers in western countries.

Introduction: Healthcare workers (HCWs) are exposed to various risk factors and risky behaviours that may seriously affect their health and ability to work. The aim of this protocol is to detail the steps to follow in order to carry out a scoping review to assess the prevalence/incidence of injuries among HCWs.

Methods And Analysis: The study will be carried out in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Protocols guidelines.

A reliability and readability analysis of silicosis-related Italian websites: implications for occupational health.

Background: Silicosis represents a "classical" occupational disease characterized by a renewed interest. New risk factors are emerging, such as sandblasting in the jeans industry or hydrofracking, leading to clusters of acute or massive cases.

Objectives: Given that the Internet could represent a worker education and empowerment tool, and considering the increase in popularity of silicosis-related information, we aimed at systematically analyzing the reliability and readability of online silicosis-relevant information.

Susceptibility to vaccine-preventable diseases and vaccination adherence among healthcare workers in Italy: A cross-sectional survey at a regional acute-care university hospital and a systematic review.

Healthcare Workers (HCWs) have an increased risk both to acquire and to spread vaccine preventable diseases (VPDs) both to their colleagues and, especially, to vulnerable patients. The prevention of occupational hazards among HCWs is based on proper adoption of the standard and additional precautions, immunizations, and secondary preventive measures, such as post-exposure prophylaxis. Moreover, HCWs are often referred to as the most trusted source of vaccine-related information for their patients.