AIMC Topic: Likelihood Functions

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A new method for clustered survival data: Estimation of treatment effect heterogeneity and variable selection.

Biometrical journal. Biometrische Zeitschrift Dec 10, 2023
We recently developed a new method random-intercept accelerated failure time model with Bayesian additive regression trees (riAFT-BART) to draw causal inferences about population treatment effect on patient survival from clustered and censored surviv...
Humans Machine Learning Likelihood Functions Treatment Effect Heterogeneity Bayes Theorem Computer Simulation
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Embedding-based terminology expansion via secondary use of large clinical real-world datasets.

Journal of biomedical informatics Sep 29, 2023
A log-likelihood based co-occurrence analysis of ∼1.9 million de-identified ICD-10 codes and related short textual problem list entries generated possible term candidates at a significance level of p<0.01. These top 10 term candidates, consisting of ...
Likelihood Functions Language Natural Language Processing Cluster Analysis
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ModelRevelator: Fast phylogenetic model estimation via deep learning.

Molecular phylogenetics and evolution Aug 16, 2023
Selecting the best model of sequence evolution for a multiple-sequence-alignment (MSA) constitutes the first step of phylogenetic tree reconstruction. Common approaches for inferring nucleotide models typically apply maximum likelihood (ML) methods, ...
Deep Learning Phylogeny Nucleotides Sequence Alignment Likelihood Functions
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Machine learning and statistical models for analyzing multilevel patent data.

Scientific reports Aug 7, 2023
A recent surge of patent applications among public hospitals in China has aroused significant research interest. A country's healthcare innovation capacity can be measured by its number of patents. This paper explores the link between the number of p...
Humans Machine Learning Likelihood Functions Cluster Analysis China Models, Statistical
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Efficient targeted learning of heterogeneous treatment effects for multiple subgroups.

Biometrics Dec 10, 2022
In biomedical science, analyzing treatment effect heterogeneity plays an essential role in assisting personalized medicine. The main goals of analyzing treatment effect heterogeneity include estimating treatment effects in clinically relevant subgrou...
Precision Medicine Humans Likelihood Functions Propensity Score Computer Simulation Machine Learning
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Estimation of Parameters on Probability Density Function Using Enhanced GLUE Approach.

Computational intelligence and neuroscience Oct 14, 2022
The most essential process in statistical image and signal processing is the parameter estimation of probability density functions (PDFs). The estimation of the probability density functions is a contentious issue in the domains of artificial intelli...
Probability Artificial Intelligence Likelihood Functions Computer Simulation Algorithms Bayes Theorem
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Missing data imputation in clinical trials using recurrent neural network facilitated by clustering and oversampling.

Biometrical journal. Biometrische Zeitschrift Mar 10, 2022
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...
Likelihood Functions Cluster Analysis Neural Networks, Computer Bias Data Interpretation, Statistical Humans
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Evaluating the robustness of targeted maximum likelihood estimators via realistic simulations in nutrition intervention trials.

Statistics in medicine Feb 16, 2022
Several recently developed methods have the potential to harness machine learning in the pursuit of target quantities inspired by causal inference, including inverse weighting, doubly robust estimating equations and substitution estimators like targe...
Computer Simulation Research Design Models, Statistical Causality Humans Machine Learning Likelihood Functions Regression Analysis
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A Survey of Blind Modulation Classification Techniques for OFDM Signals.

Sensors (Basel, Switzerland) Jan 28, 2022
Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communic...
Likelihood Functions Algorithms Support Vector Machine Machine Learning Neural Networks, Computer
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AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data.

Journal of biomedical informatics Nov 23, 2021
BACKGROUND: Scoring systems are highly interpretable and widely used to evaluate time-to-event outcomes in healthcare research. However, existing time-to-event scores are predominantly created ad-hoc using a few manually selected variables based on c...
Machine Learning Humans Likelihood Functions
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