AI Medical Compendium Topic:
Bayes Theorem

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Compositionally-warped Gaussian processes.

Neural networks : the official journal of the International Neural Network Society
The Gaussian process (GP) is a nonparametric prior distribution over functions indexed by time, space, or other high-dimensional index set. The GP is a flexible model yet its limitation is given by its very nature: it can only model Gaussian marginal...

Development of pedestrian crash prediction model for a developing country using artificial neural network.

International journal of injury control and safety promotion
Urban intersections in India constitute a significant share of pedestrian fatalities. However, model-based prediction of pedestrian fatalities is still in a nascent stage in India. This study proposes an artificial neural network (ANN) technique to d...

Efficiently searching through large tACS parameter spaces using closed-loop Bayesian optimization.

Brain stimulation
BACKGROUND: Selecting optimal stimulation parameters from numerous possibilities is a major obstacle for assessing the efficacy of non-invasive brain stimulation.

Deep learning-based selection of human sperm with high DNA integrity.

Communications biology
Despite the importance of sperm DNA to human reproduction, currently no method exists to assess individual sperm DNA quality prior to clinical selection. Traditionally, skilled clinicians select sperm based on a variety of morphological and motility ...

Computer-Aided Diagnosis and Clinical Trials of Cardiovascular Diseases Based on Artificial Intelligence Technologies for Risk-Early Warning Model.

Journal of medical systems
The use of artificial intelligence in medicine is currently an issue of great interest, especially with regard to the diagnostic or predictive analysis of medical data. In order to achieve the regional medical and public health data analysis through ...

Enhancing ontology-driven diagnostic reasoning with a symptom-dependency-aware Naïve Bayes classifier.

BMC bioinformatics
BACKGROUND: Ontology has attracted substantial attention from both academia and industry. Handling uncertainty reasoning is important in researching ontology. For example, when a patient is suffering from cirrhosis, the appearance of abdominal vein v...

Using machine learning to predict one-year cardiovascular events in patients with severe dilated cardiomyopathy.

European journal of radiology
PURPOSE: Dilated cardiomyopathy (DCM) is a common form of cardiomyopathy and it is associated with poor outcomes. A poor prognosis of DCM patients with low ejection fraction has been noted in the short-term follow-up. Machine learning (ML) could aid ...

Discriminant analysis and machine learning approach for evaluating and improving the performance of immunohistochemical algorithms for COO classification of DLBCL.

Journal of translational medicine
BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is classified into germinal center-like (GCB) and non-germinal center-like (non-GCB) cell-of-origin groups, entities driven by different oncogenic pathways with different clinical outcomes. DLBCL clas...

Cascade interpolation learning with double subspaces and confidence disturbance for imbalanced problems.

Neural networks : the official journal of the International Neural Network Society
In this paper, a new ensemble framework named Cascade Interpolation Learning with Double subspaces and Confidence disturbance (CILDC) is designed for the imbalanced classification problems. Developed from the Cascade Forest of the Deep Forest which i...