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Full-Text Articles in Medicine and Health Sciences
A Machine Learning Approach For Predicting Clinical Trial Patient Enrollment In Drug Development Portfolio Demand Planning, Ahmed Shoieb
A Machine Learning Approach For Predicting Clinical Trial Patient Enrollment In Drug Development Portfolio Demand Planning, Ahmed Shoieb
Masters Theses
One of the biggest challenges the clinical research industry currently faces is the accurate forecasting of patient enrollment (namely if and when a clinical trial will achieve full enrollment), as the stochastic behavior of enrollment can significantly contribute to delays in the development of new drugs, increases in duration and costs of clinical trials, and the over- or under- estimation of clinical supply. This study proposes a Machine Learning model using a Fully Convolutional Network (FCN) that is trained on a dataset of 100,000 patient enrollment data points including patient age, patient gender, patient disease, investigational product, study phase, blinded …
Wirelesseeg: Data Aquisition + Handheld Device, Michael Dylan Snowden, Madeline Threatt, Brent Mcferrin, David Platillero, Karsten Solies, Lindsey Hopf
Wirelesseeg: Data Aquisition + Handheld Device, Michael Dylan Snowden, Madeline Threatt, Brent Mcferrin, David Platillero, Karsten Solies, Lindsey Hopf
Chancellor’s Honors Program Projects
No abstract provided.