# Using Machine Learning Methods to Predict Treatment Outcome for Anxious Youth

> **NIH NIH F31** · TEMPLE UNIV OF THE COMMONWEALTH · 2020 · $33,509

## Abstract

PROJECT SUMMARY/ABSTRACT
Anxiety disorders in youth are highly prevalent [1] and impairing [2-4]. Left untreated, these disorders confer
substantial additional risk for the development of a wide range of negative sequelae, including substance use
[5], suicidal ideation and attempts [6], and additional mental health comorbidities [7]. Although several
treatments have demonstrated efficacy for anxiety in youth, including individual cognitive behavioral therapy
(ICBT), family CBT (FCBT), medication (MED), and combination of CBT and medication (COMB) [8], a
meaningful portion of youth are classified as non-responders after a full course of treatment [9]. The
identification of baseline predictors and moderators of response is critical to improve treatment efficacy and
reduce burden on families. Increased anxiety severity, comorbidity (behavioral problems, depression), and
family psychopathology, along with older age, female gender and anxiety diagnosis, have been highlighted as
potential predictors and moderators of outcome. However, studies have been underpowered and findings are
inconsistent [10]. To date, all studies have taken a traditional analytic approach, which typically provides
conservative estimates as a result of imposed explanatory constraints [11]. Machine Learning (ML) represents
a promising complementary statistical technique to traditional analyses, given its focus on predictive fit rather
than explanatory inference [12] and will facilitate identification of non-linear, complex patterns of predictors and
moderators at the individual level [13, 14]. These methods have shown promise in identification of treatment
outcome predictors in other medical [e.g., 15-18] and psychiatric samples [e.g., 19-22], but to date have not
been implemented in a sample of anxious youth. The proposed project will aggregate datasets from at
minimum ten peer-reviewed and published randomized controlled trials (N=1444) and train and validate two
models along overlapping features, including (1) demographics, (2) diagnosis, (3) anxiety severity (4)
behavioral problems, and (5) family psychopathology. Models will also be used to examine differential
response to ICBT, FCBT, MED and COMB. Aggregated data will be uploaded into a centralized dataset, in line
with the NIMH RDoC db and NDAR [23] datasets, and then used to predict outcome for individual anxious
youth (N=80) completing ICBT and COMB at the Child and Adolescent Anxiety Disorders Clinic at Temple. The
aims of this study are consistent with calls issued in the NIMH strategic plan (Objective 3) and will help
facilitate the development of person-centered interventions for anxious youth [24]. An individualized approach
to treatment is important to further increase treatment efficacy and reduce the financial and emotional burden
associated with non-response [25, 26]. A training plan has been designed that consists of mentorship, formal
classwork and experiential learning to develop the applicant's expertise in ...

## Key facts

- **NIH application ID:** 9990935
- **Project number:** 1F31MH123038-01
- **Recipient organization:** TEMPLE UNIV OF THE COMMONWEALTH
- **Principal Investigator:** Lesley Anne Norris
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $33,509
- **Award type:** 1
- **Project period:** 2020-07-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9990935

## Citation

> US National Institutes of Health, RePORTER application 9990935, Using Machine Learning Methods to Predict Treatment Outcome for Anxious Youth (1F31MH123038-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9990935. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*