College of Medicine
Shickel Lab
Led by Benjamin Shickel · Medicine
What we study
The Shickel Lab develops machine-learning and artificial-intelligence methods for clinical decision support, using hospital and health-record data.
What working here is like
- Computational
- Coding required
- Project
- 1
- Papers
- 5
Current projects
Artificial Intelligence Passport for Biomedical Research (AIPassportBMR)
A digital learning community to train researchers in AI for biomedical and behavioral studies.
Summaries are written by AI in plain language from public symposium abstracts and may be imperfect.
Recent papers, explained
- Goals of Care Transition after Postoperative Undertriage
2026 · Journal of the American College of Surgeons
Finds that undertriage of high-acuity patients to hospital floors is linked to new goals-of-care orders.
- The Bitter Lesson and its Implications for Surgical Artificial Intelligence
2026 · Annals of Surgery
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- MySurgeryRisk Model Predictions of Postoperative Complications and Mortality
2026 · JAMA Surgery
Tests whether a surgical risk model works well across different hospitals without losing accuracy.
- Reinforcement Learning for Intraoperative Hypotension Management with Consideration to Postoperative Acute Kidney Injury
2026 · Kidney360
Builds a reinforcement learning model to guide fluid and vasopressor use during surgery to avoid kidney injury.
- Language Models for Value-Informed Proxy Decision Support
2026 · NEJM AI
Tests whether large language models can help proxies make treatment choices that match patients' values.
Plain-language summaries are AI-written and reviewed by us; check the paper for details.
How to reach out
Benjamin Shickel hasn't set up this page yet. Their UF Scholars profile lists their publications.