Session 5 : Assessing Heterogeneity in Maternal-Child Outcomes: Insights from Machine Learning

Presenter Information/ Coauthors Information

Corneliu Bolbocean, University of ArkansasFollow

Presentation Type

Invited

Student

No

Track

Health Care Application

Abstract

This talk will explore the application of machine learning to uncover heterogeneous treatment effects in health outcomes. Drawing on my published and ongoing research, I will discuss how causal forest analysis has been utilized to identify subgroup-specific responses to medical interventions, including hip fracture treatments, the long-term health impacts of very preterm birth, and the mental health consequences of miscarriage. This work will highlight the importance of leveraging advanced methods to guide and optimize healthcare resource allocation, and address disparities in patient outcomes.

Start Date

2-7-2025 11:00 AM

End Date

2-7-2025 12:00 PM

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Feb 7th, 11:00 AM Feb 7th, 12:00 PM

Session 5 : Assessing Heterogeneity in Maternal-Child Outcomes: Insights from Machine Learning

Dakota A & C (Room 250)

This talk will explore the application of machine learning to uncover heterogeneous treatment effects in health outcomes. Drawing on my published and ongoing research, I will discuss how causal forest analysis has been utilized to identify subgroup-specific responses to medical interventions, including hip fracture treatments, the long-term health impacts of very preterm birth, and the mental health consequences of miscarriage. This work will highlight the importance of leveraging advanced methods to guide and optimize healthcare resource allocation, and address disparities in patient outcomes.