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1. HADD: High-Accuracy Detection of Depressed Mood

   https://doi.org/10.3390/technologies10060123  

   Liu, Yu ; Kang, Kyoung-Don ; Doe, Mi Jin ( December 2022 , Technologies)

   Depression is a serious mood disorder that is under-recognized and under-treated. Recent advances in mobile/wearable technology and ML (machine learning) have provided opportunities to detect the depressed moods of participants in their daily lives with their consent. To support high-accuracy, ubiquitous detection of depressed mood, we propose HADD, which provides new capabilities. First, HADD supports multimodal data analysis in order to enhance the accuracy of ubiquitous depressed mood detection by analyzing not only objective sensor data, but also subjective EMA (ecological momentary assessment) data collected by using mobile devices. In addition, HADD improves upon the accuracy of state-of-the-art ML algorithms for depressed mood detection via effective feature selection, data augmentation, and two-stage outlier detection. In our evaluation, HADD significantly enhanced the accuracy of a comprehensive set of ML models for depressed mood detection. 

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2. Stress and depressed mood increase the risk of night sweats, but not hot flashes, in menopausal women.

   Shreyer, Sofiya I ; Sievert, Lynnette L ; Brown, Daniel E. ( January 2023 , American journal of human biology)
3. Increased pupil dilation to angry faces predicts interpersonal stress generation in offspring of depressed mothers

   https://doi.org/10.1111/jcpp.12739  

   Feurer, Cope ; Burkhouse, Katie L. ; Siegle, Greg ; Gibb, Brandon E. ( August 2017 , Journal of Child Psychology and Psychiatry)
4. Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants

   https://doi.org/10.1111/jcpp.13580  

   Athreya, Arjun P. ; Vande Voort, Jennifer L. ; Shekunov, Julia ; Rackley, Sandra J. ; Leffler, Jarrod M. ; McKean, Alastair J. ; Romanowicz, Magdalena ; Kennard, Betsy D. ; Emslie, Graham J. ; Mayes, Taryn ; et al ( March 2022 , Journal of Child Psychology and Psychiatry)

   The treatment of depression in children and adolescents is a substantial public health challenge. This study examined artificial intelligence tools for the prediction of early outcomes in depressed children and adolescents treated with fluoxetine, duloxetine, or placebo.

   The study samples included training datasets (N = 271) from patients with major depressive disorder (MDD) treated with fluoxetine and testing datasets from patients with MDD treated with duloxetine (N = 255) or placebo (N = 265). Treatment trajectories were generated using probabilistic graphical models (PGMs). Unsupervised machine learning identified specific depressive symptom profiles and related thresholds of improvement during acute treatment.

   Variation in six depressive symptoms (difficulty having fun, social withdrawal, excessive fatigue, irritability, low self‐esteem, and depressed feelings) assessed with the Children’s Depression Rating Scale‐Revised at 4–6 weeks predicted treatment outcomes with fluoxetine at 10–12 weeks with an average accuracy of 73% in the training dataset. The same six symptoms predicted 10–12 week outcomes at 4–6 weeks in (a) duloxetine testing datasets with an average accuracy of 76% and (b) placebo‐treated patients with accuracies of 67%. In placebo‐treated patients, the accuracies of predicting response and remission were similar to antidepressants. Accuracies for predicting nonresponse to placebo treatment were significantly lower than antidepressants.

   PGMs provided clinically meaningful predictions in samples of depressed children and adolescents treated with fluoxetine or duloxetine. Future work should augment PGMs with biological data for refined predictions to guide the selection of pharmacological and psychotherapeutic treatment in children and adolescents with depression.

    

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