Sharon Davis

Research Assistant Professor

Vanderbilt University Medical Center

United States

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About

Sharon Davis is a Research Assistant Professor at Vanderbilt University Medical Center in the United States. Her research focuses on the cost-effectiveness of healthcare interventions, algorithmic fairness in model maintenance, and applications of machine learning in healthcare, particularly related to opioid use and risk prediction models. She has authored several recent articles addressing issues such as acute kidney injury prevention, opioid withdrawal syndrome, and enhancing medical product surveillance.

Articles (14)

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2025

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2025

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2025

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2025

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2024

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2023

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2023

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2023

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2022

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2022

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2020

Collaborators (3)

Craig Ramsay

University of Aberdeen

UNITED KINGDOM

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Christophe Gerard Lambert

Professor

University of New Mexico Health Sciences Center

UNITED STATES

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Colin Walsh

Vanderbilt University

UNITED STATES

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Sharon Davis

Research Interests

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Articles (14)

Detecting Opioid Use Disorder in Health Claims Data With Positive Unlabeled Learning.

Cost-Effectiveness of Team-Based Coaching With Surveillance for Prevention of Acute Kidney Injuries.

Emerging algorithmic bias: fairness drift as the next dimension of model maintenance and sustainability.

Adapting a Risk Prediction Tool for Neonatal Opioid Withdrawal Syndrome.

A machine learning framework to adjust for learning effects in medical device safety evaluation.

Enhancing Suicide Attempt Risk Prediction Models with Temporal Clinical Note Features.

Enhancing Postmarketing Surveillance of Medical Products With Large Language Models.

Sustainable deployment of clinical prediction tools-a 360° approach to model maintenance

Use of Electronic Health Record Data for Drug Safety Signal Identification: A Scoping Review.

Simulating complex patient populations with hierarchical learning effects to support methods development for post-market surveillance.

Use of Electronic Health Record Data for Drug Safety Signal Identification: A Scoping Review

Open questions and research gaps for monitoring and updating AI-enabled tools in clinical settings.

Maintaining a National Acute Kidney Injury Risk Prediction Model to Support Local Quality Benchmarking.

Detection of calibration drift in clinical prediction models to inform model updating

Collaborators (3)

Craig Ramsay

Christophe Gerard Lambert

Colin Walsh

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