AI Medical Compendium Topic:
Bayes Theorem

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A machine learning-based approach for predicting the outbreak of cardiovascular diseases in patients on dialysis.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Patients with End- Stage Kidney Disease (ESKD) have a unique cardiovascular risk. This study aims at predicting, with a certain precision, death and cardiovascular diseases in dialysis patients.

Predicting breast cancer metastasis by using serum biomarkers and clinicopathological data with machine learning technologies.

International journal of medical informatics
BACKGROUND: Approximately 10%-15% of patients with breast cancer die of cancer metastasis or recurrence, and early diagnosis of it can improve prognosis. Breast cancer outcomes may be prognosticated on the basis of surface markers of tumor cells and ...

A Novel Internet of Things Framework Integrated with Real Time Monitoring for Intelligent Healthcare Environment.

Journal of medical systems
During mammogram screening, there is a higher probability that detection of cancers is missed, and more than 16 percentage of breast cancer is not detected by radiologists. This problem can be solved by employing image processing algorithms which enh...

Predicting Growth and Carcass Traits in Swine Using Microbiome Data and Machine Learning Algorithms.

Scientific reports
In this paper, we evaluated the power of microbiome measures taken at three time points over the growth test period (weaning, 15 and 22 weeks) to foretell growth and carcass traits in 1039 individuals of a line of crossbred pigs. We measured predicti...

Exploiting machine learning for end-to-end drug discovery and development.

Nature materials
A variety of machine learning methods such as naive Bayesian, support vector machines and more recently deep neural networks are demonstrating their utility for drug discovery and development. These leverage the generally bigger datasets created from...

Approximating the Ideal Observer and Hotelling Observer for Binary Signal Detection Tasks by Use of Supervised Learning Methods.

IEEE transactions on medical imaging
It is widely accepted that the optimization of medical imaging system performance should be guided by task-based measures of image quality (IQ). Task-based measures of IQ quantify the ability of an observer to perform a specific task, such as detecti...

Learning Moral Graphs in Construction of High-Dimensional Bayesian Networks for Mixed Data.

Neural computation
Bayesian networks have been widely used in many scientific fields for describing the conditional independence relationships for a large set of random variables. This letter proposes a novel algorithm, the so-called -learning algorithm, for learning m...

Predicting daily outcomes in acetaminophen-induced acute liver failure patients with machine learning techniques.

Computer methods and programs in biomedicine
BACKGROUND/OBJECTIVE: Assessing prognosis for acetaminophen-induced acute liver failure (APAP-ALF) patients during the first week of hospitalization often presents significant challenges. Current models such as the King's College Criteria (KCC) and t...

Application of Artificial Intelligence for Preoperative Diagnostic and Prognostic Prediction in Epithelial Ovarian Cancer Based on Blood Biomarkers.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: We aimed to develop an ovarian cancer-specific predictive framework for clinical stage, histotype, residual tumor burden, and prognosis using machine learning methods based on multiple biomarkers.