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Objective: We aimed to determine the predictive factors for psychiatric problem severity and/or clinical approval 10 years after first assessment between ages 1-4 years. Method: The children that were assessed with the CBCL and the BITSEA in their 1-4 years of age were included. The follow-up assessments were made by telephone interviews 10 years later by child psychiatry residents. The primary caregivers responded the follow-up questionnaire items, such as social/academic/behavioral functioning and self-esteem and the items regarding the psychiatric approval, diagnosis, and treatment history. Results: The CBCL-Internalizing scores in toddlerhood were found to be significantly inversely correlated with social and behavioral functioning level and self-esteem scores. The paternal and maternal BITSEA-Competence scores were found to be positively correlated with social and academic functioning. The CBCL Withdrawn/Depressed points at first evaulation were determined to be statistically significantly predictive for psychiatric diagnosis. Conclusion: The study revealed that some measures including CBCL and BITSEA completed in toddlerhood significantly indicate the children under risk of psychiatric morbidity in adolescence. Therefore, we conclude that the widespread use of this assessment measures in toddlers and the referral of the group under risk to the psychiatry units for further investigations could be useful preventive interventions.

Usta, Mirac Baris/0000-0002-1573-3165; Sahin, Berkan/0000-0003-4699-3418 WOS: 000481832500012 OBJECTIVE Studies show partial improvements in some core symptoms of Autism Spectrum Disorders (ASD) in time. However, the predictive factors (e.g. pretreatment IQ, comorbid psychiatric disorders, adaptive, and language skills, etc.) for a better the outcome was not studied with machine learning methods. We aimed to examine the predictors of outcome with machine learning methods, which are novel computational methods including statistical estimation, information theories and mathematical learning automatically discovering useful patterns in large amounts of data. METHOD The study the group comprised 433 children (mean age: 72.3 +/- 45.9 months) with ASD diagnosis. The ASD symptoms were assessed by the Autism Behavior Checklist, Aberrant Behavior Checklist, Clinical Global Impression scales at baseline (T0) and 12th (T1), 24th (T2), and 36th (T3) months. We tested the performance of for machine learning algorithms (Naive Bayes, Generalized Linear Model, Logistic Regression, Decision Tree) on our data, including the 254 items in the baseline forms. Patients with