Adam Levine
Brown University, RI·Medicine & Clinical Research
Diarrheal diseases cause about 6.6 billion illness episodes and 1.5 million deaths each year, among both children and adults. The most important treatment is replacing lost fluids, by mouth or through an IV drip.
- Fields
Medicine & Clinical Research
Public Health & Epidemiology
- Topics
acute diarrhea
diarrheal disease
dehydration
- Grants
- 1 known grant, through Apr 2028
What they work on
- Evaluate whether the DHAKA (children under five) and NIRUDAK (patients over five) dehydration models remain accurate in new patient groups in the United States, Africa and South-East Asia.
- Externally validate the AI-based mHealth clinical decision support tool, a phone app that estimates dehydration severity and recommends rehydration fluids, across patients of all ages.
- Use an implementation science approach to identify barriers and facilitators to uptake of the mHealth app by different types of healthcare providers in Tanzania and the United States.
More about the research
Judging how dehydrated a patient is helps doctors give the right amount of fluid, which reduces harm and saves resources. Yet no validated tool exists to estimate dehydration severity across all ages. In the United States alone, diarrheal illness accounts for 1.7 million emergency visits a year among children.
Why it matters
A validated phone app that estimates dehydration could help doctors, nurses and community health workers give the right type and amount of fluids to patients with diarrhea. This could reduce deaths and complications from missed or wrong dehydration diagnoses and avoid wasted resources in clinics. It could reach hundreds of millions of patients each year worldwide, including millions in the United States.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Andrew Bishara
University of California-San Francisco, CA·Medicine & Clinical Research·Early-career
Implement machine learning models that predict perioperative acute kidney injury in real time during surgery and alert clinicians while kidney-protective treatments can still be given.
Nosheen Reza
University of Pennsylvania, PA·Medicine & Clinical Research·Early-career
Build and validate a machine learning model that detects hypertrophic cardiomyopathy from electrocardiograms in Penn Medicine electronic health record data, and test whether adding other EHR-derived traits improves detection.
Elizabeth Keating
University of Utah, UT·Medicine & Clinical Research·Early-career
Describe barriers to care for injured children at health centers and district hospitals in Tanzania, the first level of medical contact, using a mixed methods approach.
The school
Brown University: 23 labs on PI Pages, $375M a year on research, 251 research doctorates a year.
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