AI Medical Compendium Topic:
Bayes Theorem

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Automated quantification of myocardial tissue characteristics from native T mapping using neural networks with uncertainty-based quality-control.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Tissue characterisation with cardiovascular magnetic resonance (CMR) parametric mapping has the potential to detect and quantify both focal and diffuse alterations in myocardial structure not assessable by late gadolinium enhancement. Nat...

Bayesian machine learning to discover Bruton's tyrosine kinase inhibitors.

Chemical biology & drug design
Bruton's tyrosine kinase (BTK) has a crucial role in multiple cell signaling pathways including B-cell antigen receptor (BCR) and Fc receptor (FcR) signaling cascades, which has attracted much attention to find BTK inhibitors to treat autoimmune dise...

Active Learning of Bayesian Linear Models with High-Dimensional Binary Features by Parameter Confidence-Region Estimation.

Neural computation
In this letter, we study an active learning problem for maximizing an unknown linear function with high-dimensional binary features. This problem is notoriously complex but arises in many important contexts. When the sampling budget, that is, the num...

Synthesis of recurrent neural dynamics for monotone inclusion with application to Bayesian inference.

Neural networks : the official journal of the International Neural Network Society
We propose a top-down approach to construct recurrent neural circuit dynamics for the mathematical problem of monotone inclusion (MoI). MoI in a general optimization framework that encompasses a wide range of contemporary problems, including Bayesian...

Improving the accuracy of medical diagnosis with causal machine learning.

Nature communications
Machine learning promises to revolutionize clinical decision making and diagnosis. In medical diagnosis a doctor aims to explain a patient's symptoms by determining the diseases causing them. However, existing machine learning approaches to diagnosis...

Machine learning improves mortality risk prediction after cardiac surgery: Systematic review and meta-analysis.

The Journal of thoracic and cardiovascular surgery
BACKGROUND: Interest in the usefulness of machine learning (ML) methods for outcomes prediction has continued to increase in recent years. However, the advantage of advanced ML model over traditional logistic regression (LR) remains controversial. We...

Long-distance disorder-disorder relation extraction with bootstrapped noisy data.

Journal of biomedical informatics
OBJECTIVE: Artificial intelligence in healthcare increasingly relies on relations in knowledge graphs for algorithm development. However, many important relations are not well covered in existing knowledge graphs. We aim to develop a novel long-dista...

Predicting Absenteeism and Temporary Disability Using Machine Learning: a Systematic Review and Analysis.

Journal of medical systems
The main objective of this paper is to present a systematic analysis and review of the state of the art regarding the prediction of absenteeism and temporary incapacity using machine learning techniques. Moreover, the main contribution of this resear...

Design of Festival Sentiment Classifier Based on Social Network.

Computational intelligence and neuroscience
With the development of society, more and more attention has been paid to cultural festivals. In addition to the government's emphasis, the increasing consumption in festivals also proves that cultural festivals are playing increasingly important rol...

Twin minimax probability machine for pattern classification.

Neural networks : the official journal of the International Neural Network Society
We propose a new distribution-free Bayes optimal classifier, called the twin minimax probability machine (TWMPM), which combines the benefits of both minimax probability machine(MPM) and twin support vector machine (TWSVM). TWMPM tries to construct t...