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Machine Learning Methods to Develop and Deploy Real-Time Risk Surveillance for Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder from the Electronic Health Record

Award Number: K01MH127309
ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP
PERIOD OF PERFORMANCE START DATE: 09/01/2022
PERIOD OF PERFORMANCE END DATE: 08/31/2027

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Machine Learning Methods to Develop and Deploy Real-Time Risk Surveillance for Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder from the Electronic Health Record - Early treatment of autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD) can improve long-term outcomes, but only when at-risk children are identified promptly and accurately. Early risk factors common to both conditions (e.g. premature birth, perinatal complications) are collected during routine care and are accessible in the electronic health record (EHR), but have not been used at scale to stratify risk or improve early screening. The central premise of the proposed research is that the EHR contains information that can help identify children at risk for ASD and ADHD early in development, but translating this information into clinically actionable measures requires new predictive modeling methods. Our recent NIMH-supported research provides initial support for this premise. We have discovered that children later diagnosed with ASD and/or ADHD have distinctive patterns of early health system utilization, and that EHR data acquired by age 1 already contains information predictive of later ASD risk. Building on this work, the proposed research will develop and deploy ASD and ADHD risk prediction models that can directly inform clinical decision-making. First, we propose to develop methods and prediction models that will optimize prediction performance, ensure predictions are not affected by disparities in utilization, and surveil model-predicted risk over time to identify children as early as possible without loss of accuracy. Second, we propose to take critical steps toward clinical use by prospectively evaluating these models, deploying them in the Duke University Health System (DUHS) EHR, and working with providers to develop and evaluate a prototype EHR dashboard that presents ASD and ADHD risk and projected follow-up length to inform clinical decision-making. Career development goals will allow the candidate to develop expertise needed to establish an independent, transdisciplinary research program in machine learning for pediatric mental health. Career training will be tightly integrated with the proposed research and emphasize deployment, evaluation, and implementation of risk prediction models for ASD and ADHD. In the proposed study, DUHS EHR data will be accessed through a high-quality, empirically validated data pipeline for all children born after 10/1/2006. Data elements will include diagnosis and procedure codes, lab measures and vitals, clinical notes, and encounter details. Diagnoses will be identified using ICD-10-based criteria previously validated at DUHS. The study will begin with creation of a secure, regularly updated diagnosis risk prediction database to facilitate a smooth transition to EHR deployment. Activities will focus on methods and model development in years 1-2 followed by development, deployment, and evaluation of the risk prediction dashboard beginning in year 3. This research will dovetail with concurrent and subsequent work exploring novel ASD screening methods and model-guided interventions at DUHS, including patient- and provider-centered early intervention strategies. The proposed career development will support the PI’s transition to independence and health data science initiatives at Duke.


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 = $162,893 )
2025	2025	DUKE UNIVERSITY	2200 W MAIN ST	DURHAM	NC	27705	DURHAM	USA	Mental Health Research Grants	001	4	9/3/2025	NON-COMPETING CONTINUATION	-$7,721
2025	2025	DUKE UNIVERSITY	2200 W MAIN ST	DURHAM	NC	27705	DURHAM	USA	Mental Health Research Grants	000	4	8/14/2025	NON-COMPETING CONTINUATION	$170,614
														Subtotal = $162,893

Issue Date FY: 2024 ( Subtotal = $171,146 )
2024	2024	DUKE UNIVERSITY	2200 W MAIN ST	DURHAM	NC	27705	DURHAM	USA	Mental Health Research Grants	000	3	8/14/2024	NON-COMPETING CONTINUATION	$171,146
														Subtotal = $171,146

Issue Date FY: 2023 ( Subtotal = $172,144 )
2023	2023	DUKE UNIVERSITY	2200 W MAIN ST	DURHAM	NC	27705	DURHAM	USA	Mental Health Research Grants	000	2	9/1/2023	NON-COMPETING CONTINUATION	$172,144
														Subtotal = $172,144

Issue Date FY: 2022 ( Subtotal = $172,144 )
2022	2022	DUKE UNIVERSITY	2200 W MAIN ST STE 710	DURHAM	NC	27708	DURHAM	USA	Mental Health Research Grants	000	1	9/1/2022	NEW	$172,144
														Subtotal = $172,144

Grand Total All Awards = $678,327

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Machine Learning Methods to Develop and Deploy Real-Time Risk Surveillance for Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder from the Electronic Health Record

Award Number: K01MH127309

ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP

PERIOD OF PERFORMANCE START DATE: 09/01/2022

PERIOD OF PERFORMANCE END DATE: 08/31/2027

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