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Quantitative dataset audit methods to identify bias, shortcut, and robustness risks for data-driven healthcare AI

open

NLM - National Library of Medicine

Project Summary Machine learning and artificial intelligence (ML/AI) promise to enhance the quality, safety, efficiency, efficacy, affordability, and accessibility of healthcare. The anticipated benefits are evident from the surge in FDA ap- provals for AI/ML-based “software as a medical device (SaMD)”, increasing to over 600 in 2023. Approval requires adequate algorithm performance under controlled test conditions, but AI/ML solutions often deteriorate significantly in real-world settings, with controlled tests overestimating ML/AI performance by 20% or more. Thus, real-world deployment of ML/AI may yield substantially lower benefits or even net losses. The brittleness of ML/AI models is fundamentally linked to the data they are trained and tested on. Dataset bias can manifest in many ways, leading to unfair outcomes, poor generalization, and unpredictable behavior of AI/ML models. Identify- ing and quantifying dataset bias is thus most significant to prevent erroneous analyses, statistical fallacies and representational errors when using these datasets to develop and test AI/ML solutions. This proposal aims to develop automated techniques to identify dataset biases likely to affect down- stream ML/AI models. Using testbeds in medical image analysis, we will identify bias in the form of dataset shortcuts – non-clinically relevant features leaking task-relevant information, and imaging conditions – image properties associated with a specific clinical setting. In contrast to prior work that has focused on trained ML/AI models, our methods will audit datasets explicitly and independently of any assumptions about the ML/AI models that may use these datasets. This approach will help AI/ML SaMD stakeholders quantify and measure how well ML/AI methods address undesirable biases. Our specific aims are: 1) A framework and testbeds for assessing and validating dataset audits: We will create a framework to manipulate dataset bias, shortcuts, and imaging conditions for testing our auditing methods. We will curate datasets for chest X-ray, CT, and dermascopy, including public and in-house data, and define tasks for each modality, demonstrating the generality and utility of dataset auditing across varied medical imaging modalities. 2) Causality-driven auditing methods to identify and measure medical imaging dataset bias and shortcut learning risk: We will develop techniques to identify image-based signatures linked to data factors spuriously associated with outcomes, i. e., shortcuts, that lead to poor ML/AI performance. 3) Causality-driven auditing method to assess the robustness risk: We will assess how dataset composition with respect to image quality affects training and testing of AI/ML models, and using causal models of image formation, will identify image generation factors that limit model robustness. While our developments focus on ML/AI-based medical image analysis due to its rapid clinical translation, the concepts are broadly applicable to other ML/AI-based digital health tools, contributing to the development of trustworthy AI/ML digital health tools that transform healthcare delivery.

Up to $589K
2031-04-30
health research

Free to search & build · $99 one-time to unlock the application pack · No subscription

Saltzman Road Apartments

open

CUMC Affordable Housing

Saltzman Road Apartments

Up to $125K
Rolling
infrastructure

Free to search & build · $99 one-time to unlock the application pack · No subscription

Saltzman Road Apartments

open

CUMC Affordable Housing

Saltzman Road Apartments

Up to $125K
Rolling
infrastructure

Free to search & build · $99 one-time to unlock the application pack · No subscription

Saltzman Road Apartments

open

CUMC Affordable Housing

Saltzman Road Apartments

Up to $250K
Rolling
infrastructure

Free to search & build · $99 one-time to unlock the application pack · No subscription

Solid Phase Peptide Synthesizer

open

OD - NIH Office of the Director

ABSTRACT/SUMMARY This proposal requests funds to purchase a Liberty Blue 2.0 solid-phase peptide synthesizer. At present, Vanderbilt lacks a comparable capacity for customized peptide synthesis, compelling researchers to rely on commercial vendors. While standard peptides can often be sourced at reasonable cost, the synthesis of peptides incorporating non-proteinogenic amino acids, macrocyclizations, or site-specific chemical modifications incurs prohibitive costs and prolonged lead times. These limitations negatively affect numerous NIH-funded research programs and severely lowers the chemical novelty accessible to investigators who make use of peptides in their research. Acquisition of an institutional instrument will directly address this gap, enabling timely and affordable access to high-quality, customized peptides that are increasingly central to modern biomedical research. This instrument will serve a large and scientifically expansive group of investigators across 15 departments in the College of Arts and Science, the School of Medicine Basic Sciences, and the Vanderbilt Institute of Chemical Biology. Investigators from the Vanderbilt University Medical Center will also have access. The user base spans a wide array of NIH-funded projects that rely on synthetic peptides. For example, one group synthesizes fluorophore-labeled peptides to monitor receptor trafficking. Another develops cleavable linkers that release antibiotics from antibody-drug conjugates designed to target methicillin-resistant Staphylococcus aureus. A third focuses on macrocyclic peptides that modulate the activity of CFTR and thus show promise as future therapeutics for cystic fibrosis. Several other groups engage heavily in structure- and AI-guided design and require rapid synthesis of candidate molecules to support downstream biochemical and cellular validation. The Liberty Blue 2.0, manufactured by CEM Corporation, uses microwave-assisted chemistry to accelerate synthesis cycles, improve coupling efficiency, and enhance overall yield and purity. The instrument accommodates a wide range of chemistries and scales, offering flexibility to support exploratory screening, structure-activity relationship campaigns, and early-stage preclinical development. Importantly, it also provides significant cost and time savings compared to commercial synthesis, especially for chemically complex sequences. The instrument will be housed within the Molecular Design and Synthesis Core, which has provided synthetic chemistry expertise and training to the Vanderbilt community since 2006. This core will oversee daily operation and user access, supported by administrative and financial contributions from the School of Medicine Basic Sciences and the College of Arts and Science. Acquisition of the Liberty Blue 2.0 will significantly enhance Vanderbilt’s infrastructure for chemical biology, lower the barrier to peptide-based experimentation, and accelerate discovery across multiple scientific disciplines and therapeutic categories.

Up to $129K
2027-05-14
health research

Free to search & build · $99 one-time to unlock the application pack · No subscription

The Spillover Effect of Medicaid Expansion on Children's Nutrition Security

open

NHLBI - National Heart Lung and Blood Institute

PROJECT SUMMARY Low food security and diet-related diseases are a major public health concern and disproportionately impact families from low-income households. Too many families choose between paying for housing, utilities, food, and healthcare. To help fulfill these basic needs, Medicaid provides 39 million low-income households with children access to vital healthcare services for long-term health and well-being. To reach more individuals from low-income households, the Affordable Care Act (ACA) intended to expand Medicaid coverage to nearly all adults nationwide with incomes <138% of the federal poverty level, however the Supreme Court ruled that states could not be coerced to expand their Medicaid programs, effectively rendering the expansion optional. To date, most of the research on the ACA Medicaid expansion has focused on the policy’s impact on insurance coverage, access to and use of healthcare, and health among adults, as well as health insurance coverage for children. Our study will fill a critical gap in the literature by being the first to estimate the causal impact of ACA Medicaid expansion on food security and diet quality in children, an extremely important outcome because it is tied to physical growth and development, cognitive development, academic performance, and lifelong health. Our long-term goal is to inform strategies to promote food security through understanding the spillover effects of ACA Medicaid expansion on food security and diet quality in children. To achieve our goal, we will address 3 specific aims leveraging national data on low-income households with children with which we have extensive experience: 1) Examine whether ACA Medicaid expansion increased federal nutrition assistance participation in households with children using the Current Population Survey’s Annual Social and Economic Supplement (CPS-ASEC) and the Survey of Income and Program Participation (SIPP), a household panel survey; 2) Examine whether ACA Medicaid expansion improved food security in children using CPS-Food Security Supplement (CPS-FSS), the key source of food security status of U.S. children, and SIPP; and 3) Examine whether ACA Medicaid expansion increased household diet quality in children using the Circana National Consumer Panel, a household panel survey of detailed food purchases that can be linked to estimate overall nutritional quality of Americans’ food-at-home purchases. This will be the first study to comprehensively examine an important spillover effect of ACA Medicaid expansion on improving children’s food security and diet quality. Findings will help us understand the likely spillover effects of expanding Medicaid eligibility—or, as in more recent legislation, reducing Medicaid eligibility for parents—on nutrition outcomes for children from low- income households.

Up to $693K
2031-05-31
health research

Free to search & build · $99 one-time to unlock the application pack · No subscription

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