Used NLP to audit the research on GLP-1 drugs and binge eating, accepted at an IEEE conference

Research on GLP-1 drugs, the class behind today's weight-loss medications, is exploding faster than anyone can read it. Athena asked whether a transparent language-processing pipeline could sort that literature the way expert reviewers do. Working with 1,422 records from a curated evidence map, she built a classifier that labels findings on brain measures, craving, binge outcomes, appetite and metabolic outcomes, using study-grouped cross-validation so no study leaked between training and testing. Her model reached an F1 score of 0.894 on appetite and satiety outcomes.

FIELDMedical AI & Natural Language Processing

RESULTFirst-author paper accepted at an IEEE conference

VENUEIEEE, 2026

BEFORE THE FELLOWSHIP

A 12th grader at Leigh High School in San Jose, California interested in medicine and data, with no published research.

AFTER

First author of a transparent NLP pipeline for auditing medical evidence on GLP-1 drugs and binge eating disorder, accepted at an IEEE conference.

THE LEDGER

01First-author paper accepted at an IEEE conference

02Classified 1,422 medical literature records across five outcome labels

03F1 score of 0.894 on appetite and satiety outcomes

04Study-grouped cross-validation to prevent data leakage

NEXT CASE STUDYHarleen Badwal, First-author paper accepted at an IEEE conference

Every case study starts with one application.

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