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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.

The purpose of this study was to explore associations between specific types of hallucinations and delusions and suicidal ideation in a sample of children and adolescents with bipolar I disorder.

Participants (N = 379) were children and adolescents aged 6‐15 years (M = 10.2, SD = 2.7) with DSM‐IV diagnoses of bipolar I disorder, mixed or manic phase. The study sample was 53.8% female and primarily White (73.6% White, 17.9% Black, and 8.5% Other). Presence and nature of psychotic symptoms, suicidal ideation, and functioning level were assessed through clinician‐administered measures. A series of logistic regressions was performed to assess the contribution of each subtype of psychotic symptom to the presence of suicidal ideation above and beyond age, sex, socio‐economic status, age at bipolar disorder onset, and global level of functioning.

Hallucinations overall, delusions of guilt, and number of different psychotic symptom types were uniquely associated with increased odds of suicidal ideation after accounting for covariates. Other forms of delusions (eg, grandiose) and specific types of hallucinations (eg, auditory) were not significantly uniquely associated with the presence of suicidal ideation.

Findings of this study suggest the presence of hallucinations as a whole, delusions of guilt specifically, and having multiple concurrent types of psychotic symptoms are associated with the presence of suicidal ideation in children and adolescents with bipolar I disorder. Psychotic symptom subtypes, as opposed to psychosis as a whole, are an under‐examined, potentially important, area for consideration regarding suicidal ideation in pediatric bipolar I disorder.