Decision trees for early prediction of inadequate immune response to coronavirus infections: a pilot study on COVID-19

Introduction: Few artificial intelligence models exist to predict severe forms of COVID-19. Most rely on post-infection laboratory data, hindering early treatment for high-risk individuals. Methods: This study developed a machine learning model to predict inherent risk of severe symptoms after contracting SARS-CoV-2. Using a Decision Tree trained on 153 Alpha variant patients, demographic, clinical and immunogenetic markers were considered. Model performance was assessed on Alpha and Delta variant datasets. Key risk factors included age, gender, absence of KIR2DS2 gene (alone or with HLA-C C1 group alleles), presence of 14-bp polymorphism in HLA-G gene, presence of KIR2DS5 gene, and presence of KIR telomeric region A/A. Results: The model achieved 83.01% accuracy for Alpha variant and 78.57% for Delta variant, with True Positive Rates of 80.82 and 77.78%, and True Negative Rates of 85.00% and 79.17%, respectively. The model showed high sensitivity in identifying individuals at risk. Discussion: The present study demonstrates the potential of AI algorithms, combined with demographic, epidemiologic, and immunogenetic data, in identifying individuals at high risk of severe COVID-19 and facilitating early treatment. Further studies are required for routine clinical integration

Repetition and severity of suicide attempts across the life cycle: a comparison by age group between suicide victims and controls with severe depression

<p>Abstract</p> <p>Background</p> <p>Suicide attempts have been shown to be less common in older age groups, with repeated attempts generally being more common in younger age groups and severe attempts in older age groups. Consistently, most studies have shown an increased suicide risk after attempts in older age. However, little is known about the predictive value of age on repeated and severe suicide attempts for accomplished suicide. The aim of the present study was to investigate the reduced incidence for initial, repeated, or severe suicide attempts with age in suicide victims and controls by gender.</p> <p>Methods</p> <p>The records of 100 suicide victims and matched controls with severe depression admitted to the Department of Psychiatry, Lund University Hospital, Sweden between 1956 and 1969, were evaluated and the subjects were monitored up to 2006. The occurrence of suicide attempts (first, repeated, or severe, by age group) was analysed for suicide victims and controls, with gender taken into consideration.</p> <p>Results</p> <p>There was a reduced risk for an initial suicide attempt by older age in females (suicide victims and controls) and male controls (but not suicide victims). The risk for repeated suicide attempts appeared to be reduced in the older age groups in female controls as compared to female suicide victims. The risk for severe suicide attempts seemed reduced in the older age groups in female suicide victims. This risk was also reduced in male controls and in male controls compared to male suicide victims.</p> <p>Conclusion</p> <p>In the older age groups repeated attempts appeared to be predictive for suicide in women and severe attempts predictive in men.</p