Renee Manworren
University of Texas at Arlington, TX·Medicine & Clinical Research
Pain is hard to measure and easy to mistreat. Some patients cannot report it at all, and others face bias when they seek relief.
- Fields
Medicine & Clinical Research
Computer Science
Public Health & Epidemiology
- Topics
sickle cell disease
vaso-occlusive crisis
chronic pain
- Grants
- 2 known grants, through Aug 2030
What they work on
- Collect pain-related gene variants, NIH HEAL common data elements, social determinants of health and other biopsychosocial data over time from 1,000 Black adolescents and young adults with sickle cell disease in three U.S. regions.
- Train and test a computational model that predicts frequent hospitalization for vaso-occlusive crisis pain in sickle cell disease and evaluates the effectiveness of drug treatments, including opioids.
- Train and test predictive models that estimate the age at which chronic daily pain begins in Black adolescents and young adults with sickle cell disease.
- Build a diverse dataset of facial action video, physiological and clinical data from more than 200 newborns before and after surgery, to develop an AI pain-recognition model.
- Test a hybrid federated learning approach, which trains AI across hospital units without sharing raw data, on newborn pain data, and prove its convergence for convex learning problems.
More about the research
These projects build data-driven tools to recognize and predict pain more precisely in two vulnerable groups: Black adolescents and young adults with sickle cell disease, and hospitalized newborns.
Why it matters
Young people with sickle cell disease face severe pain, frequent hospitalization, opioid risks and racial bias in pain treatment. Risk models based on genetics and social factors could support precision pain management for them. For hospitalized newborns who cannot report pain, an AI monitoring system supervised by nurses could reduce under- and over-treatment. Privacy-preserving federated learning could also help hospitals learn from shared data on rare conditions.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Enrico Novelli
University of Pittsburgh, PA·Medicine & Clinical Research
Determine whether hemin, a toxic breakdown product of hemoglobin, and hypoxia (low oxygen) trigger VLA-4-driven blood-cell stickiness in sickle cell mice, measured by PET imaging with the tracer 64Cu-LLP2A.
Rafal Pawlinski
University of North Carolina at Chapel Hill, NC·Medicine & Clinical Research
Identify the disease-specific triggers that activate Factor XII, a protein that starts blood clotting, in sickle cell disease, using blood samples from patients and a mouse model of the disease.
Kenneth Ataga
University of Tennessee Health Science Center, TN·Medicine & Clinical Research
Build a machine learning model that predicts chronic kidney disease progression in adults with sickle cell disease, using data from a prospective, multicenter study.
The school
University of Texas at Arlington: 11 labs on PI Pages, $154M a year on research, 177 research doctorates a year.
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