Allison Summers
Oregon Health & Science University, OR·Medicine & Clinical Research
Amblyopia, commonly called lazy eye, affects 2–4% of children and can permanently reduce vision if untreated. Treatment — such as patching the stronger eye — is cheap and effective, but it only works if families stick with it, and adherence is the main obstacle.
- Fields
Medicine & Clinical Research
Computer Science
Public Health & Epidemiology
- Topics
amblyopia
lazy eye
pediatric ophthalmology
- Grants
- 1 known grant, through Jul 2027
What they work on
- Develop large language model pipelines that extract amblyopia treatment, adherence and visual outcome information from free-text ophthalmology clinical notes, where these concepts are documented inconsistently and not in structured EHR fields.
- Evaluate and validate extraction accuracy against reference annotations using electronic health record data from a single institution.
- Test how well the models transfer across institutions using multi-site records from the Sight Outcomes Research Collaborative (SOURCE) consortium, where documentation practices differ.
- Demonstrate that the extracted data can support research on amblyopia treatment and adherence and could underpin clinical decision support tools used during care.
More about the research
Studying how treatment, adherence and visual outcomes play out in ordinary clinical practice is hard, because clinics record these details inconsistently and usually only in free-text notes rather than in structured, searchable fields of the electronic health record (EHR). As a result, large real-world datasets about amblyopia care remain largely unusable.
Why it matters
If treatment and adherence details can be read reliably out of routine eye clinic notes, researchers could study amblyopia care in tens of thousands of real children rather than small trials, and clinicians could be alerted when a child is falling behind on patching. That could help more children keep vision that is otherwise lost to a treatable condition.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Selen Bozkurt
Emory University, GA·Medicine & Clinical Research
Translate clinical trial protocol eligibility text into computable phenotypes and query packages tied to standard vocabularies, and validate them against expert references and prescreening logs.
Catherine Sun
University of California-San Francisco, CA·Medicine & Clinical Research·Early-career
Develop an automated method to identify patients with proliferative diabetic retinopathy in electronic health records, combining structured data such as diagnosis codes, medications and labs with unstructured clinical notes.
Yili Zhang
Georgetown University, DC·Computer Science
Develop a large language model–based natural language processing pipeline that extracts immune-related adverse event evidence, type and grade from de-identified clinical notes, creating a reusable annotated corpus.
The school
Oregon Health & Science University: 24 labs on PI Pages, $545M a year on research, 54 research doctorates a year.
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