Proposing an AI Passport as a Mitigating Action of Risk Associated to Artificial Intelligence in Healthcare.

The integration of Artificial Intelligence (AI) in healthcare signifies a substantial shift, offering benefits to patients and healthcare systems while also introducing new risks. The emphasis on patient safety and performance standards is pivotal, especially with the European Union's strides towards regulating AI through the AI Act. This act focuses on classifying AI systems based on risk levels, mandating stringent requirements for high-risk AI, enhancing transparency, and ensuring ethics in AI applications.

User-centred design of a clinical decision support system for palliative care: Insights from healthcare professionals.

Objective: Although clinical decision support systems (CDSS) have many benefits for clinical practice, they also have several barriers to their acceptance by professionals. Our objective in this study was to design and validate The palliative care (PC) CDSS through a user-centred method, considering the predictions of the artificial intelligence (AI) core, usability and user experience (UX).

Methods: We performed two rounds of individual evaluation sessions with potential users.

Complementary frailty and mortality prediction models on older patients as a tool for assessing palliative care needs.

Palliative care (PC) has demonstrated benefits for life-limiting illnesses. Bad survival prognosis and patients' decline are working criteria to guide PC decision-making for older patients. Still, there is not a clear consensus on when to initiate early PC.

How the Wakamola chatbot studied a university community's lifestyle during the COVID-19 confinement.

This work aimed to study the effect of confinement on weight and lifestyle using the Wakamola chatbot to collect data from 739 adults divided into two groups (341 case-control, 398 confinement). Nutrition score (0-100 scale) improved for men (medians 81.77-82.

A user-centered chatbot to identify and interconnect individual, social and environmental risk factors related to overweight and obesity.

The objective of this study was to assess the feasibility of using a user-centered chatbotfor collecting linked data to study overweight and obesity causes ina target population. In total 980 people participated in the feasibility study organized in three studies: (1) within a group of university students (88 participants), (2) in a small town (422 participants), and (3) within a university community (470 participants). We gathered self-reported data through the Wakamola chatbot regarding participants diet, physical activity, social network, living area, obesity-associated diseases, and sociodemographic data.

Design of 1-year mortality forecast at hospital admission: A machine learning approach.

Palliative care is referred to a set of programs for patients that suffer life-limiting illnesses. These programs aim to maximize the quality of life (QoL) for the last stage of life. They are currently based on clinical evaluation of the risk of 1-year mortality.

Machine Learning-Based Identification of Obesity from Positive and Unlabelled Electronic Health Records.

Introduction: Prevalence of overweight and obesity are increas- ing in the last decades, and with them, diseases and health conditions such as diabetes, hypertension or cardiovascular diseases. However, hos- pital databases usually do not record such conditions in adults, neither anthropomorfic measures that facilitate their identification.

Methods: We implemented a machine learning method based on PU (Positive and Unlabelled) Learning to identify obese patients without a diagnose code of obesity in the health records.