Publications by authors named "T Torti"
Personalized psycho-oncology represents a major challenge for the holistic care of cancer patients. It focuses on individualized psychotherapeutic and psychiatric interventions to address specific psychological needs. This narrative review summarizes the current literature on personalized psycho-oncology and highlights the prevalence and impact of psychiatric/psychological disorders in cancer patients.
View Article and Find Full Text PDFThis chapter provides a comprehensive examination of a broad range of biomarkers used for the diagnosis and prediction of treatment outcomes in major depressive disorder (MDD). Genetic, epigenetic, serum, cerebrospinal fluid (CSF), and neuroimaging biomarkers are analyzed in depth, as well as the integration of new technologies such as digital phenotyping and machine learning. The intricate interplay between biological and psychological elements is emphasized as essential for tailoring MDD management strategies.
View Article and Find Full Text PDFBackground: We investigated, for the first time, whether there are any sex differences in retrospective self-reported childhood maltreatment (CM) in Italian adult patients with major depressive disorder (MDD) or bipolar disorder (BD). Furthermore, the potential impacts of patients' age on the CM self-report were investigated.
Methods: This retrospective study used the data documented in the electronic medical records of patients who were hospitalized for a 4-week psychiatric rehabilitation program.
Article Synopsis
- - The study analyzed the rates of first-onset major depression (PMDD) among Italian adults during the COVID-19 pandemic and developed a machine learning model to predict these occurrences in future samples.
- - The research involved 3,532 participants and found that 7.4% in the first wave and 7.2% in the second wave experienced PMDD, with key factors including low resilience, being an undergraduate student, pandemic-related stress, and poor sleep quality.
- - Despite limitations like a small sample size and reliance on self-reports, the study concluded that depression rates were significant and the machine learning model showed promise for future interventions to prevent depression during public health crises.
Predicting New-Onset Psychiatric Disorders Throughout the COVID-19 Pandemic: A Machine Learning Approach.
J Neuropsychiatry Clin Neurosci
August 2022
Objective: The investigators estimated new-onset psychiatric disorders (PsyDs) throughout the COVID-19 pandemic in Italian adults without preexisting PsyDs and developed a machine learning (ML) model predictive of at least one new-onset PsyD in subsequent independent samples.
Methods: Data were from the first (May 18-June 20, 2020) and second (September 15-October 20, 2020) waves of an ongoing longitudinal study, based on a self-reported online survey. Provisional diagnoses of PsyDs (PPsyDs) were assessed via DSM-based screening tools to maximize assessment specificity.