AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Likelihood Functions

Showing 11 to 20 of 86 articles

Clear Filters

Automatic Differentiation is no Panacea for Phylogenetic Gradient Computation.

Genome biology and evolution
Gradients of probabilistic model likelihoods with respect to their parameters are essential for modern computational statistics and machine learning. These calculations are readily available for arbitrary models via "automatic differentiation" implem...

ModelRevelator: Fast phylogenetic model estimation via deep learning.

Molecular phylogenetics and evolution
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, ...

Machine learning and statistical models for analyzing multilevel patent data.

Scientific reports
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...

Embedding-based terminology expansion via secondary use of large clinical real-world datasets.

Journal of biomedical informatics
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 ...

Fusang: a framework for phylogenetic tree inference via deep learning.

Nucleic acids research
Phylogenetic tree inference is a classic fundamental task in evolutionary biology that entails inferring the evolutionary relationship of targets based on multiple sequence alignment (MSA). Maximum likelihood (ML) and Bayesian inference (BI) methods ...

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

Biometrical journal. Biometrische Zeitschrift
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...

Deep Learning from Phylogenies for Diversification Analyses.

Systematic biology
Birth-death (BD) models are widely used in combination with species phylogenies to study past diversification dynamics. Current inference approaches typically rely on likelihood-based methods. These methods are not generalizable, as a new likelihood ...

Consideration on the learning efficiency of multiple-layered neural networks with linear units.

Neural networks : the official journal of the International Neural Network Society
In the last two decades, remarkable progress has been done in singular learning machine theories on the basis of algebraic geometry. These theories reveal that we need to find resolution maps of singularities for analyzing asymptotic behavior of stat...

Artificial neural networks for model identification and parameter estimation in computational cognitive models.

PLoS computational biology
Computational cognitive models have been used extensively to formalize cognitive processes. Model parameters offer a simple way to quantify individual differences in how humans process information. Similarly, model comparison allows researchers to id...

Deep Learning and Likelihood Approaches for Viral Phylogeography Converge on the Same Answers Whether the Inference Model Is Right or Wrong.

Systematic biology
Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences about the phylodynamics and history of viral transmission. However, these methods are often computation...