College of Medicine
Rahman Lab
Led by Protiva Rahman · Health Outcomes and Biomedical Informatics
What we study
The Rahman Lab builds tools to make data science accessible for healthcare experts. They focus on automating data extraction from medical notes and literature using AI and natural language processing.
What working here is like
- Computational
- Coding required
- Projects
- 3
- Papers
- 5
Current projects
Applications of Deep Learning and Generative Artificial Intelligence (AI) to Find Fractures
Applies deep learning and generative AI to medical images and records to find bone fractures, aiming to support clinicians in detecting them.
Development, Evaluation, and Dissemination of Semantic Data Quality Standards for Multi-Center Clinical Research Studies and Networks
Creating standards to improve data quality in clinical research.
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CvMOBISU2: CardioVascular Multicenter OBservational Investigation of lipid care in the United States
Analyzes data from a multi-center observational study of cholesterol and lipid care for cardiovascular patients in the United States, using data science tools.
Summaries are written by AI in plain language from public symposium abstracts and may be imperfect.
Recent papers, explained
- Characterizing Continuous Glucose Monitoring Prescription Patterns Between 2020 and 2024: A Retrospective Study at a Single Primary Care Facility in the United States
2026 · Journal of Diabetes Research
Examines factors linked to continuous glucose monitoring prescriptions in diabetes patients.
- Development and validation of a predictive model using clinical, molecular, and social determinants of health data to improve Hepatitis C detection in underserved populations
2026 · American Journal of Transplantation
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- Language Models for Multilabel Document Classification of Surgical Concepts in Exploratory Laparotomy Operative Notes: Algorithm Development Study
2025 · JMIR Medical Informatics
Develops a tool to automatically extract surgical concepts from medical notes.
- Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data
2024 · JCO Clinical Cancer Informatics
Builds a model to predict immune checkpoint inhibitor effectiveness and side effects using patient data.
- Aggregation and Contextualization of Murine Investigations Improves Discovery of Significant Human Atherosclerotic Cardiovascular Disease Associations
2024 · Circulation
Combines data from mouse studies to find links to human heart disease.
Plain-language summaries are AI-written and reviewed by us; check the paper for details.
How to reach out
Protiva Rahman hasn't set up this page yet. You can also look them up in the UF directory (search “Protiva Rahman”).