A novel machine learning model for prediction of ICI responsive patients based on ICI pharmacokinetics
TS-075183 — Problem
Current eligibility biomarkers for Immune checkpoint inhibitors (ICIs) poorly predict patient’s response. Drug clearance, both at baseline and over time, is a stronger predictor of ICI effectiveness, however the current non-linear mixed effects modeling (NLME) approach for estimating …
- College: College of Pharmacy
- Inventors: Phelps, Mitchell "Mitch"; Adeluola, Adeoluwa; Coss, Christopher; Kim, Kyeongmin; Mo, Xiaokui "Molly"; Owen, Dwight
- Licensing Officer: Hampton, Andrew
EmitGCL: Prediction of Future Metastasis based on Graph Contrastive Learning
TS-075168 — Problem
Metastasis is the primary driver of cancer-related deaths, yet accurately predicting its onset is a major clinical challenge. Current diagnostic tools often fail to detect occult metastatic cells and metastatic precursor cells, resulting in either overdiagnosis, with unnecessary treatments,…
- College: College of Medicine (COM)
- Inventors: Ma, Qin; Wang, Xiaoying
- Licensing Officer: Hampton, Andrew
Patient Access Prioritization Score
TS-063212 — When a patient demonstrates signs of imminent clinical deterioration, it is essential to immediately assess and treat the patient to prevent intensive care unit transfer, cardiac arrest, or death. These tasks often include transferring the patient to other facilities with optimal capabilities.
T…
- College: College of Medicine (COM)
- Inventors: Reinbolt, Raquel; Cohn, David
- Licensing Officer: Hampton, Andrew
Convolutional Neural Network to Assess Phayngeal and Laryngeal Pathology and Function on Nasopharyngolaryngoscopy
TS-063154 — Worldwide, 686,000 new head and neck (H&N) cancers are diagnosed yearly, and 375,000 people will die annually. Human papillomavirus (HPV) is responsible for an increasing subset of H&N malignancies called oropharyngeal squamous cell carcinomas (OPSCC). Although it has a better prognosis than…
- College: College of Engineering (COE)
- Inventors: Krening, Samantha; Gifford, Ryan; Jhawar, Sachin; VanKoevering, Kyle
- Licensing Officer: Hampton, Andrew
SimPi: A Remote Power Platform for Simulation
TS-059056 — A platform for improving simulations across many industries.
Developing real-life simulations of disasters is vital for safety and reducing their financial impact; however, current simulation tools are bulky, expensive to implement, and require advanced technical skills to operate.
Simulations are essential for evaluating the potential impacts of disasters and for creating effective preparedness and response plans to facilitate organized and coordinated actions. Since response plans are not theoretical exercises, they must frequently be tested to be evaluated, adapted, and u…
- College: College of Medicine (COM)
- Inventors: Winfield, Scott; Beck, James; Finnegan, Geoff
- Licensing Officer: Hampton, Andrew
Finding rare events by knowing where NOT to look
TS-051417 —
Traditional regression analysis has been a staple in predicting cause-effect relationships. Counltess industries use these methods to predict rare events, from economic issues to cancer research, causality is often a desired result. Given the vast amount of potential variables it sometimes becomes…
- College: College of Arts and Sciences (ASC)
- Inventors: Melamed, David; Schoon, Eric
- Licensing Officer: Hampton, Andrew
