# Machine learning-assisted precision mental health for cigarette smoking cessation

> **NIH NIH F31** · UNIVERSITY OF WISCONSIN-MADISON · 2022 · $46,752

## Abstract

Project Summary
 The goal of this fellowship application is to support and facilitate the necessary training for the applicant
to develop an independent research career in precision mental health for substance use disorders. Her long-
term program of research will 1) identify and harness data sources that may allow for differential treatment
selection, 2) build machine learning models to select among treatments, 3) bring these models forward for use
in clinical practice, and 4) use these models to inform the development of novel treatments to fill gaps in care.
Through the proposed training goals, guided mentorship, and complementary experiences, this fellowship will
strategically advance the applicant’s career. She will increase her knowledge of substance use disorders and
treatments. She will gain expertise in advanced quantitative methods including feature engineering, genetic
analyses, and machine learning. She will complete her training with the skills to conduct and disseminate
interdisciplinary research poised to further precision mental health research for substance use disorders.
 The proposed project seeks to apply machine learning to precision mental health for cigarette smoking
cessation. Precision mental health is the application of the precision medicine paradigm to mental health
conditions. Precision mental health guides treatment selection using individual differences characteristics likely
to predict treatment success. Several factors have hindered progress towards successful treatment selection
via precision mental health. First, traditional analytic techniques have been insufficient to account for the real-
world complexities that underlie treatment response and recovery. Second, precision mental health models are
built and evaluated in the same sample and consequently do not generalize to new data (i.e., new patients).
Third, precision mental health research has rarely included genetic features (predictors) alongside and in
interaction with clinical (non-genetic) data, preventing integration of knowledge across domains.
 Contemporary machine learning approaches are well-suited to address these limitations. Machine
learning models can accommodate high-dimensional arrays of features across data sources, extract reliable
prediction signals that generalize robustly to new samples of patients, and incorporate genetic and non-genetic
features simultaneously. The proposed project will apply machine learning to the precision mental health
paradigm for cigarette smoking cessation. Cigarette smoking remains a critical and costly public health crisis
for which existing treatments are only moderately effective at best. The proposed project will produce a model
that can guide treatment selection among several first-line (i.e., FDA-approved) smoking cessation
medications. Successful application of the precision mental health paradigm to cigarette smoking cessation
would have immediate clinical impact by accelerating and optimizing therapeut...

## Key facts

- **NIH application ID:** 10464420
- **Project number:** 1F31DA056144-01
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Gaylen E Fronk
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $46,752
- **Award type:** 1
- **Project period:** 2022-06-19 → 2025-06-18

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10464420

## Citation

> US National Institutes of Health, RePORTER application 10464420, Machine learning-assisted precision mental health for cigarette smoking cessation (1F31DA056144-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10464420. Licensed CC0.

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