AI Medical Compendium Topic:
Bayes Theorem

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Robust Optimization and Validation of Echo State Networks for learning chaotic dynamics.

Neural networks : the official journal of the International Neural Network Society
An approach to the time-accurate prediction of chaotic solutions is by learning temporal patterns from data. Echo State Networks (ESNs), which are a class of Reservoir Computing, can accurately predict the chaotic dynamics well beyond the predictabil...

A comprehensive scoping review of Bayesian networks in healthcare: Past, present and future.

Artificial intelligence in medicine
No comprehensive review of Bayesian networks (BNs) in healthcare has been published in the past, making it difficult to organize the research contributions in the present and identify challenges and neglected areas that need to be addressed in the fu...

PlncRNA-HDeep: plant long noncoding RNA prediction using hybrid deep learning based on two encoding styles.

BMC bioinformatics
BACKGROUND: Long noncoding RNAs (lncRNAs) play an important role in regulating biological activities and their prediction is significant for exploring biological processes. Long short-term memory (LSTM) and convolutional neural network (CNN) can auto...

Detection of deep myometrial invasion in endometrial cancer MR imaging based on multi-feature fusion and probabilistic support vector machine ensemble.

Computers in biology and medicine
The depth of myometrial invasion affects the treatment and prognosis of patients with endometrial cancer (EC), conventionally evaluated using MR imaging (MRI). However, only a few computer-aided diagnosis methods have been reported for identifying de...

Mixed-precision weights network for field-programmable gate array.

PloS one
In this study, we introduced a mixed-precision weights network (MPWN), which is a quantization neural network that jointly utilizes three different weight spaces: binary {-1,1}, ternary {-1,0,1}, and 32-bit floating-point. We further developed the MP...

Leveraging voxel-wise segmentation uncertainty to improve reliability in assessment of paediatric dysplasia of the hip.

International journal of computer assisted radiology and surgery
PURPOSE: Estimating uncertainty in predictions made by neural networks is critically important for increasing the trust medical experts have in automatic data analysis results. In segmentation tasks, quantifying levels of confidence can provide meani...

Evaluating advanced computing techniques for predicting breeding values in Harnali sheep.

Tropical animal health and production
Advanced computing techniques have been used by animal researchers to understand the intricate data structures for deriving the most reliable allusions of populations in order to conserve genetically superior animals. The present attempt was made to ...

An incremental learning approach to automatically recognize pulmonary diseases from the multi-vendor chest radiographs.

Computers in biology and medicine
The human respiratory network is a vital system that provides oxygen supply and nourishment to the whole body. Pulmonary diseases can cause severe respiratory problems, leading to sudden death if not treated timely. Many researchers have utilized dee...

A comprehensive review and analysis of supervised-learning and soft computing techniques for stress diagnosis in humans.

Computers in biology and medicine
Stress is the most prevailing and global psychological condition that inevitably disrupts the mood and behavior of individuals. Chronic stress may gravely affect the physical, mental, and social behavior of victims and consequently induce myriad crit...

Tongue image quality assessment based on a deep convolutional neural network.

BMC medical informatics and decision making
BACKGROUND: Tongue diagnosis is an important research field of TCM diagnostic technology modernization. The quality of tongue images is the basis for constructing a standard dataset in the field of tongue diagnosis. To establish a standard tongue ima...