Psychiatry Grand Rounds: Prediction Modeling of ADHD and Its Outcomes- Toward Precision Psychiatry

Presented by:
Stephen Faraone, Ph.D.
Distinguished Professor and Vice Chair for Research
Department of Psychiatry

Norton College of Medicine, SUNY Upstate Medical University

Faculty Disclosure:

Dr. Faraone has disclosed that he is on the Research Support team for Otsuka and Supernus as well as a Stock Shareholder for Ironshore, Akili, and Genomind. He is also a consultant for Aardvark, Aardwolf, AIMH, Tris, Otsuka, Ironshore, Kanjo, KemPharm/Corium, Akili, Supernus, Atentiv, Noven, Sky Therapeutics, Axsome, and Genomind. He is an expert witness for Johnson & Johnson and Kenvue as well as provides support for Educational Programs at Supernus, Corium, and Otsuka. No one else in a position to control content has any financial relationships to disclose. Conflict of interest information for the CME Advisory Committee members can be found on the following website: All relevant financial relationships have been mitigated.

Release Date: November 17, 2023
Expiration Date:  November 16, 2026

Target Audience: All physicians

Learning Objectives:

As a result of participation in this activity, participants should be able to:

  1. Evaluate the adequacy of a predictive modeling study.
  2. Distinguish between predictive modeling results that are and are not useful in clinical practice.
  3. Describe the value of using predictive modeling to predict response to a non-stimulant medication for ADHD.
  4. Critique a prediction modeling study from the perspective of health care disparities.

Requirements for successful completion: Certificates are awarded upon successful completion (80% proficiency) of the post-test.

Accreditation: The University of Florida College of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.

Credit: The University of Florida College of Medicine designates this enduring material for a maximum of 1 AMA PRA Category 1 Credit™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Resource(s) for further study:

  1. Ng, A. Machine Learning Yearning: Technical Strategy for AI Engineers in the Era of Deep Learning. 2018.
  2. Barnett E, Onete D, Salekin A, Faraone S. Genomic Machine Learning Meta-regression: Insights on Associations of Study Features with Reported Model Performance. medRxiv 2022.01.10.22268751. doi:
  3. Barnett E, Zhang-James Y, Faraone S. Improving Machine Learning Prediction of ADHD Using Gene Set Polygenic Risk Scores and Risk Scores from Genetically Correlated Phenotypes. medRxiv 2022.01.11.22269027. Doi:
  6. Faraone S, Gomeni R, Hull JT, Busse GD, Melyan Z, O’Neal W, Rubin J, Nasser A. Early response to SPN-812 (viloxazine extended-release) can predict efficacy outcome in pediatric subjects with ADHD: a machine learning post-hoc analysis of four randomized clinical trials. Psychiatry Research, 2021, 296:113664.
  7. Faraone S, Gomeni R, Hull JT, Chaturvedi SA, Busse GD, Melyan Z, O’Neal W, Rubin J, Nasser A. Predicting efficacy of viloxazine extended-release treatment in adults with ADHD using an early change in ADHD symptoms: Machine learning Post Hoc analysis of a phase 3 clinical trial. Psychiatry Research, 2022, 318:114922.
  8. Garcia-Argibay M, Zhang-James Y, Cortese S, Lichtenstein P, Larsson H, Faraone SV. Predicting childhood and adolescent attention-deficit/hyperactivity disorder onset: a nationwide deep learning approach. Molecular Psychiatry, 28, 1232-1239. 2023.


If you have any questions please feel free to contact Nancy Boyd at (352) 594-4298 or at