Unmet Need:
There are many complex clinical scenarios for which the ability to predict and then optimize patient outcomes at an individual level could be beneficial. These include direct individualization of therapy for patient (i.e. bedside decision support systems) and the optimization of the design of clinical trials by providing an in-silico simulation of outcomes under varying conditions. Unfortunately, these kinds of predictions are currently possible only when algorithms have been specifically designed for a given purpose and in the same patient population. In many cases, particularly for complex medical conditions, these types of algorithms are not available.
Technology Overview:
Johns Hopkins researchers have generated a computational platform that harnesses multiple algorithms aimed at optimizing clinical management to maximize beneficial patient outcomes. This platform can be personalized to guide therapy for individual patients. This technology can also evaluate a specific therapeutic by analyzing a group of patients and predict the effect of the drug and the factors associated with positive or negative effects thereby encompassing the potential to optimizing the design of clinical trials.
Stage of Development:
Prototype of the platform exists.
Publication:
- WO 2024/155490 - Methods, Systems, and Related Aspects for the Automated Optimization and Individualization of Clinical Management
