10 citations · 12 across the 4 of their papers we have counts for
5 papers
A Staged Approach using Machine Learning and Uncertainty Quantification to Predict the Risk of Hip Fracture
Anjum Shaik, Kristoffer Larsen, Nancy E. Lane +8
Despite advancements in medical care, hip fractures impose a significant burden on individuals and healthcare systems. This paper focuses on the prediction of hip fracture risk in…
A new method of modeling the multi-stage decision-making process of CRT using machine learning with uncertainty quantification
Kristoffer Larsen, Chen Zhao, Joyce Keyak +7
Aims. The purpose of this study is to create a multi-stage machine learning model to predict cardiac resynchronization therapy (CRT) response for heart failure (HF) patients. This…
A new method using deep transfer learning on ECG to predict the response to cardiac resynchronization therapy
Zhuo He, Hongjin Si, Xinwei Zhang +3
Background: Cardiac resynchronization therapy (CRT) has emerged as an effective treatment for heart failure patients with electrical dyssynchrony. However, accurately predicting wh…
A new method using deep learning to predict the response to cardiac resynchronization therapy
Kristoffer Larsena, Zhuo He, Chen Zhao +8
Background. Clinical parameters measured from gated single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI) have value in predicting cardiac resynchroni…
A new method using machine learning to integrate ECG and gated SPECT MPI for Cardiac Resynchronization Therapy Decision Support on behalf of the VISION-CRT
Fernando de A. Fernandes, Kristoffer Larsen, Zhuo He +7
Cardiac resynchronization therapy (CRT) has been established as an important therapy for heart failure. Mechanical dyssynchrony has the potential to predict responders to CRT. The…