Development and validation of a machine learning-based vocal predictive model for major depressive disorder.

BACKGROUND: Variations in speech intonation are known to be associated with changes in mental state over time. Behavioral vocal analysis is an algorithmic method of determining individuals' behavioral and emotional characteristics from their vocal patterns. It can provide biomarkers for use in psychiatric assessment and monitoring, especially when remote assessment is needed, such as in the COVID-19 pandemic. The objective of this study was to design and validate an effective prototype of automatic speech analysis based on algorithms for classifying the speech features related to MDD using a remote assessment system combining a mobile app for speech recording and central cloud processing for the prosodic vocal patterns. METHODS: Machine learning compared the vocal patterns of 40 patients diagnosed with MDD to the patterns of 104 non-clinical participants. The vocal patterns of 40 patients in the acute phase were also compared to 14 of these patients in the remission phase of MDD. RESULTS: A vocal depression predictive model was successfully generated. The vocal depression scores of MDD patients were significantly higher than the scores of the non-patient participants (p < 0.0001). The vocal depression scores of the MDD patients in the acute phase were significantly higher than in remission (p < 0.02). LIMITATIONS: The main limitation of this study is its relatively small sample size, since machine learning validity improves with big data. CONCLUSIONS: The computerized analysis of prosodic changes may be used to generate biomarkers for the early detection of MDD, remote monitoring, and the evaluation of responses to treatment.

Comparison of symptoms and treatments of adults and adolescents with borderline personality disorder.

The purpose of this paper was to characterize adolescent borderline personality disorder (BPD) and compare it to adult BPD. A retrospective chart review of 20 adolescent and 20 adult BPD patients was conducted. The retrieved data included demographics, history features, symptoms, observations made during hospitalization and treatments. There were many similarities between the two groups. The differences included the number of report pages during hospitalization (p<0.05), of current self-mutilation (p=0.051), of past and present obsessive-compulsive symptoms (p=0.02 and p=0.03) and of past escapes (p=0.006). Adults had more alcohol abuse (p=0.02). There was a correlation between the number of anti-psychotic drug trials and the length of hospitalization among adolescents, and a correlation between the number of antidepressant and anxiolytic drug trials and the length of hospitalization among adults. Our findings support the general similarity of BPD between adolescents and adults. The differences in pharmacotherapy and several clinical observations warrant further study.