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Monte Carlo Method

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Bayesian averaging over Decision Tree models for trauma severity scoring.

Artificial intelligence in medicine
Health care practitioners analyse possible risks of misleading decisions and need to estimate and quantify uncertainty in predictions. We have examined the "gold" standard of screening a patient's conditions for predicting survival probability, based...

Estimating causal effects for survival (time-to-event) outcomes by combining classification tree analysis and propensity score weighting.

Journal of evaluation in clinical practice
RATIONALE, AIMS AND OBJECTIVES: A common approach to assessing treatment effects in nonrandomized studies with time-to-event outcomes is to estimate propensity scores and compute weights using logistic regression, test for covariate balance, and then...

Detection of Sources in Non-Negative Blind Source Separation by Minimum Description Length Criterion.

IEEE transactions on neural networks and learning systems
While non-negative blind source separation (nBSS) has found many successful applications in science and engineering, model order selection, determining the number of sources, remains a critical yet unresolved problem. Various model order selection me...

Diffusion parameter mapping with the combined intravoxel incoherent motion and kurtosis model using artificial neural networks at 3 T.

NMR in biomedicine
Artificial neural networks (ANNs) were used for voxel-wise parameter estimation with the combined intravoxel incoherent motion (IVIM) and kurtosis model facilitating robust diffusion parameter mapping in the human brain. The proposed ANN approach was...

Deep reconstruction model for dynamic PET images.

PloS one
Accurate and robust tomographic reconstruction from dynamic positron emission tomography (PET) acquired data is a difficult problem. Conventional methods, such as the maximum likelihood expectation maximization (MLEM) algorithm for reconstructing the...

Deep learning ensemble with asymptotic techniques for oscillometric blood pressure estimation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: This paper proposes a deep learning based ensemble regression estimator with asymptotic techniques, and offers a method that can decrease uncertainty for oscillometric blood pressure (BP) measurements using the bootstrap and...

A statistical framework for biomedical literature mining.

Statistics in medicine
In systems biology, it is of great interest to identify new genes that were not previously reported to be associated with biological pathways related to various functions and diseases. Identification of these new pathway-modulating genes does not onl...

Predicting Protein-protein Association Rates using Coarse-grained Simulation and Machine Learning.

Scientific reports
Protein-protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large ...

Using classification tree analysis to generate propensity score weights.

Journal of evaluation in clinical practice
RATIONALE, AIMS AND OBJECTIVES: In evaluating non-randomized interventions, propensity scores (PS) estimate the probability of assignment to the treatment group given observed characteristics. Machine learning algorithms have been proposed as an alte...

Princeton_TIGRESS 2.0: High refinement consistency and net gains through support vector machines and molecular dynamics in double-blind predictions during the CASP11 experiment.

Proteins
Protein structure refinement is the challenging problem of operating on any protein structure prediction to improve its accuracy with respect to the native structure in a blind fashion. Although many approaches have been developed and tested during t...