[HTML][HTML] Computational neuroimaging strategies for single patient predictions
Neuroimaging increasingly exploits machine learning techniques in an attempt to achieve
clinically relevant single-subject predictions. An alternative to machine learning, which tries …
clinically relevant single-subject predictions. An alternative to machine learning, which tries …
Dynamically controlled environment agriculture: Integrating machine learning and mechanistic and physiological models for sustainable food cultivation
Inefficiencies and imprecise input control in agriculture have caused devastating
consequences to ecosystems. Urban controlled environment agriculture (CEA) is a …
consequences to ecosystems. Urban controlled environment agriculture (CEA) is a …
The role of machine learning in neuroimaging for drug discovery and development
Neuroimaging has been identified as a potentially powerful probe for the in vivo study of
drug effects on the brain with utility across several phases of drug development spanning …
drug effects on the brain with utility across several phases of drug development spanning …
[HTML][HTML] Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects
Structural imaging based on MRI is an integral component of the clinical assessment of
patients with potential dementia. We here propose an individualized Gaussian process …
patients with potential dementia. We here propose an individualized Gaussian process …
Hybridizing qualitative coding with natural language processing and deep learning to assess public comments: a case study of the clean power plan
Public comments are a rich source of data on attitudes toward public policy, but the scale
poses major challenges for qualitative analyses. Supervised deep learning and natural …
poses major challenges for qualitative analyses. Supervised deep learning and natural …
Digital twins and hybrid modelling for simulation of physiological variables and stroke risk
One of the more interesting ideas for achieving personalized, preventive, and participatory
medicine is the concept of a digital twin. A digital twin is a personalized computer model of a …
medicine is the concept of a digital twin. A digital twin is a personalized computer model of a …
[HTML][HTML] Personalized medication response prediction for attention-deficit hyperactivity disorder: learning in the model space vs. learning in the data space
Attention-Deficit Hyperactive Disorder (ADHD) is one of the most common mental health
disorders amongst school-aged children with an estimated prevalence of 5% in the global …
disorders amongst school-aged children with an estimated prevalence of 5% in the global …
A hierarchical model for integrating unsupervised generative embedding and empirical Bayes
Background Generative models of neuroimaging data, such as dynamic causal models
(DCMs), are commonly used for inferring effective connectivity from individual subject data …
(DCMs), are commonly used for inferring effective connectivity from individual subject data …
[HTML][HTML] Hybrid modelling for stroke care: Review and suggestions of new approaches for risk assessment and simulation of scenarios
Stroke is an example of a complex and multi-factorial disease involving multiple organs,
timescales, and disease mechanisms. To deal with this complexity, and to realize Precision …
timescales, and disease mechanisms. To deal with this complexity, and to realize Precision …
Brain shaving: adaptive detection for brain PET data
E Grecchi, OM Doyle, A Bertoldo… - Physics in Medicine …, 2014 - iopscience.iop.org
The intricacy of brain biology is such that the variation of imaging end-points in health and
disease exhibits an unpredictable range of spatial distributions from the extremely localized …
disease exhibits an unpredictable range of spatial distributions from the extremely localized …