Aims. Prospective studies on the mental health of university students highlighted a major
concern. Specifically, young adults in academia are affected by markedly worse mental health
status than their peers or adults in other vocations. This situation predisposes to exacerbated
disability-adjusted life-years.
Methods. We enroled 1,388 students at the baseline, 557 of whom completed follow-up after
6 months, incorporating their demographic information and self-report questionnaires on
depressive, anxiety and obsessive–compulsive symptoms. We applied multiple regression modelling to determine associations – at baseline – between demographic factors and self-reported
mental health measures and supervised machine learning algorithms to predict the risk of
poorer mental health at follow-up, by leveraging the demographic and clinical information
collected at baseline.
Results. Approximately one out of five students reported severe depressive symptoms and/or
suicidal ideation. An association of economic worry with depression was evidenced both at
baseline (when high-frequency worry odds ratio = 3.11 [1.88–5.15]) and during follow-up.The
random forest algorithm exhibited high accuracy in predicting the students who maintained
well-being (balanced accuracy = 0.85) or absence of suicidal ideation but low accuracy for
those whose symptoms worsened (balanced accuracy = 0.49). The most important features
used for prediction were the cognitive and somatic symptoms of depression. However, while
the negative predictive value of worsened symptoms after 6 months of enrolment was 0.89, the
positive predictive value is basically null.
Conclusions. Students’ severe mental health problems reached worrying levels, and demographic factors were poor predictors of mental health outcomes. Further research including
people with lived experience will be crucial to better assess students’ mental health needs and
improve the predictive outcome for those most at risk of worsening symptoms.

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