Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review.

Background:  Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific clinical prediction models (CPMs) have been used to assist physicians in decision-making but have several limitations. This systematic review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient data derived from electronic health records.

AVDOS-VR: Affective Video Database with Physiological Signals and Continuous Ratings Collected Remotely in VR.

Investigating emotions relies on pre-validated stimuli to evaluate induced responses through subjective self-ratings and physiological changes. The creation of precise affect models necessitates extensive datasets. While datasets related to pictures, words, and sounds are abundant, those associated with videos are comparatively scarce.

Outcome Prediction in Critically-Ill Patients with Venous Thromboembolism and/or Cancer Using Machine Learning Algorithms: External Validation and Comparison with Scoring Systems.

Intensive care unit (ICU) patients with venous thromboembolism (VTE) and/or cancer suffer from high mortality rates. Mortality prediction in the ICU has been a major medical challenge for which several scoring systems exist but lack in specificity. This study focuses on two target groups, namely patients with thrombosis or cancer.

The epidemic volatility index, a novel early warning tool for identifying new waves in an epidemic.

Early warning tools are crucial for the timely application of intervention strategies and the mitigation of the adverse health, social and economic effects associated with outbreaks of epidemic potential such as COVID-19. This paper introduces, the Epidemic Volatility Index (EVI), a new, conceptually simple, early warning tool for oncoming epidemic waves. EVI is based on the volatility of newly reported cases per unit of time, ideally per day, and issues an early warning when the volatility change rate exceeds a threshold.

Explicit and implicit emotional expression in bulimia nervosa in the acute state and after recovery.

Expression of emotional state is considered to be a core facet of an individual's emotional competence. Emotional processing in BN has not been often studied and has not been considered from a broad perspective. This study aimed at examining the implicit and explicit emotional expression in BN patients, in the acute state and after recovery.

Video games as a complementary therapy tool in mental disorders: PlayMancer, a European multicentre study.

Background: Previous review studies have suggested that computer games can serve as an alternative or additional form of treatment in several areas (schizophrenia, asthma or motor rehabilitation). Although several naturalistic studies have been conducted showing the usefulness of serious video games in the treatment of some abnormal behaviours, there is a lack of serious games specially designed for treating mental disorders.

Aim: The purpose of our project was to develop and evaluate a serious video game designed to remediate attitudinal, behavioural and emotional processes of patients with impulse-related disorders.