Monday, August 25, 2025 8/25/2025

Machine Learning for Precision Treatments in Schizophrenia

Award Number: K23MH129628
ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP
PERIOD OF PERFORMANCE START DATE: 09/05/2022
PERIOD OF PERFORMANCE END DATE: 08/31/2026

Group Awards By:

View Award Description

Machine Learning for Precision Treatments in Schizophrenia - Project Summary/Abstract Schizophrenia is associated with psychotic symptoms, mood disturbances, deficits in cognition, comorbidities, significant social and functional impairment and is a leading cause of disability in the U.S. and worldwide. Although antipsychotic medications and psychosocial treatments are effective for some symptoms of schizophrenia, effective regimens for all symptoms are not established. The primary limitation of treatment guidelines is reliance on RCTs that test limited treatments and their effects on few symptoms and comorbidities. Trials of treatments administered to address all aspects of impairment is prohibitively complex. Data driven machine learning (ML) can address this gap using large observational datasets with information about complex and effective regimens used in real-world practice. ML can cluster individuals with shared characteristics and identify unique regimens administered for their psychiatric and clinical comorbidities. These new treatment regimens are possible precision treatments. ML algorithms can then predict critical patient-centered outcomes for these different clusters (or classes) administered these treatment regimens. Examining the comparative effectiveness of these treatment regimens that predict critical outcomes is an essential next step. Unique pharmacoepidemiologic methods with observational data can simulate clinical trials. Propensity score methods address confounding, mimicking balance achieved by randomization in RCTs. These tools will determine which precision treatment regimens are the most effective for the classes in these datasets. Relevance of ML findings depends on data quality. Claims have the largest, most nationally representative samples reflecting real-world community practice patterns but use billing codes not originally designed for research. Electronic health records (EHR) are extensive but limited due to bias from incomplete records with uncertain accuracy and complexity due to their granular level of detail. This proposal will establish the strengths and limitations of these dataset types by conducting ML analyses on exemplar datasets, a Medicaid Analytic eXtract (MAX) national sample, and the Observational Health Data Sciences and Informatics (OHDSI) network New York-Presbyterian Hospital (iNYP) EHR. An enhancement to this project will compare more traditional multivariate and regression techniques to the ML findings identifying whether ML provides additional information. To address the “research-practice” gap the ML results will be translated into personalized treatment rules to inform clinical practice for schizophrenia treatment. After training in unsupervised and supervised learning in Training Aims A and B, Research Aim 1 will identify classes and their administered treatments in the datasets and Research Aim 2 will predict outcomes of those treatments: time to emergency department visit, time to re-admission and incidence of comorbidities. Research Aim 3 will use pharmacoepidemiologic methods learned in Training Aim C to compare effectiveness of the treatments, supporting an R01 submitted at the end of this K-award to test effectiveness in an international EHR dataset.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2025 ( Subtotal = $195,478 )
2025	2025	RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC.	1051 RIVERSIDE DR	NEW YORK	NY	10032	NEW YORK	USA	Mental Health Research Grants	000	4	7/18/2025	NON-COMPETING CONTINUATION	$195,478
														Subtotal = $195,478

Issue Date FY: 2024 ( Subtotal = $195,479 )
2024	2024	RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC.	1051 RIVERSIDE DR	NEW YORK	NY	10032	NEW YORK	USA	Mental Health Research Grants	000	3	8/14/2024	NON-COMPETING CONTINUATION	$195,479
														Subtotal = $195,479

Issue Date FY: 2023 ( Subtotal = $195,479 )
2023	2023	RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC.	1051 RIVERSIDE DR	NEW YORK	NY	10032	NEW YORK	USA	Mental Health Research Grants	000	2	9/1/2023	NON-COMPETING CONTINUATION	$195,479
2023	2023	RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC.	1051 RIVERSIDE DR	NEW YORK	NY	10032	NEW YORK	USA	Mental Health Research Grants	001	2	9/26/2023	NON-COMPETING CONTINUATION	$0
														Subtotal = $195,479

Issue Date FY: 2022 ( Subtotal = $195,480 )
2022	2022	RESEARCH FOUNDATION FOR MENTAL HYGIENE INC	1051 RIVERSIDE DR	NEW YORK	NY	10032	NEW YORK	USA	Mental Health Research Grants	000	1	9/5/2022	NEW	$195,480
														Subtotal = $195,480

Grand Total All Awards = $781,916

Top

All Categories

About

Search

Reports

Data Submission

Award Information

Machine Learning for Precision Treatments in Schizophrenia

Award Number: K23MH129628

ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP

PERIOD OF PERFORMANCE START DATE: 09/05/2022

PERIOD OF PERFORMANCE END DATE: 08/31/2026

Federal Websites

Department of Health & Human Services

HHS Operating Divisions

HHS Staff Divisions

Download A Document Viewer