Search Results for author:
Found 6 papers, 0 papers with code
Nurse-in-the-Loop Artificial Intelligence for Precision Management of Type 2 Diabetes in a Clinical Trial Utilizing Transfer-Learned Predictive Digital Twin
Participants were randomly assigned to an intervention (AI, n=10) group to receive daily AI-generated individualized feedback or a control group without receiving the daily feedback (non-AI, n=10) in the last three months.
Discrimination of Radiologists Utilizing Eye-Tracking Technology and Machine Learning: A Case Study
We include a clinical trial case study utilizing the Area Under the Curve (AUC), Accuracy, F1, Sensitivity, and Specificity metrics for class separability to evaluate the discriminability between the two subjects in regard to their level of experience.
Machine learning tools to improve nonlinear modeling parameters of RC columns
Machine learning tools in the Scikit-learn and Pytorch libraries were used to calibrate equations and black-box numerical models for nonlinear modeling parameters (MP) a and b of reinforced concrete columns defined in the ASCE 41 and ACI 369. 1 standards, and to estimate their most likely mode of failure.
A Model Predictive Control Functional Continuous Time Bayesian Network for Self-Management of Multiple Chronic Conditions
Multiple chronic conditions (MCC) are one of the biggest challenges of modern times.
Nonlinear State Space Modeling and Control of the Impact of Patients' Modifiable Lifestyle Behaviors on the Emergence of Multiple Chronic Conditions
The emergence and progression of multiple chronic conditions (MCC) over time often form a dynamic network that depends on patient's modifiable risk factors and their interaction with non-modifiable risk factors and existing conditions.
A Functional Model for Structure Learning and Parameter Estimation in Continuous Time Bayesian Network: An Application in Identifying Patterns of Multiple Chronic Conditions
Bayesian networks are powerful statistical models to study the probabilistic relationships among set random variables with major applications in disease modeling and prediction.
