Computational and mathematical methods in medicine
34880930
BACKGROUND: Studies focusing on prediction models are widespread in medicine. There is a trend in applying machine learning (ML) by medical researchers and clinicians. Over the years, multiple ML algorithms have been adapted to censored data. However...
Machine learning algorithms, including recent advances in deep learning, are promising for tools for detection and classification of broadband high frequency signals in passive acoustic recordings. However, these methods are generally data-hungry and...
Computational intelligence and neuroscience
35371239
With the exponential growth of the Internet population, scientists and researchers face the large-scale data for processing. However, the traditional algorithms, due to their complex computation, are not suitable for the large-scale data, although th...
In clinical practice, the composition of missing data may be complex, for example, a mixture of missing at random (MAR) and missing not at random (MNAR) assumptions. Many methods under the assumption of MAR are available. Under the assumption of MNAR...
We present a general framework for causal mediation analysis using nonparametric Bayesian methods in the potential outcomes framework. Our model, which we refer to as the Bayesian causal mediation forests model, combines recent advances in Bayesian m...
CPT: pharmacometrics & systems pharmacology
36346135
Missing data create challenges in clinical research because they lead to loss of statistical power and potentially to biased results. Missing covariate data must be handled with suitable approaches to prepare datasets for pharmacometric analyses, suc...
CPT: pharmacometrics & systems pharmacology
36385744
As formal causal inference begins to play a greater role in disciplines that intersect with pharmacometrics, such as biostatistics, epidemiology, and artificial intelligence/machine learning, pharmacometricians may increasingly benefit from a basic f...
In recent years, machine learning methods have become increasingly popular prediction methods in psychology. At the same time, psychological researchers are typically not only interested in making predictions about the dependent variable, but also in...
The missing data mechanism is a relevant problem in Machine Learning (ML) and biomedical informatics communities. Real-world Electronic Health Record (EHR) datasets comprise several missing values, thus revealing a high level of spatiotemporal sparsi...