AIMC Topic: Bayes Theorem

Clear Filters Showing 1531 to 1540 of 1906 articles

Detection of clinically important colorectal surgical site infection using Bayesian network.

The Journal of surgical research
BACKGROUND: Despite extensive efforts to monitor and prevent surgical site infections (SSIs), real-time surveillance of clinical practice has been sparse and expensive or nonexistent. However, natural language processing (NLP) and machine learning (i...

Evaluation of supervised machine-learning algorithms to distinguish between inflammatory bowel disease and alimentary lymphoma in cats.

Journal of veterinary diagnostic investigation : official publication of the American Association of Veterinary Laboratory Diagnosticians, Inc
Inflammatory bowel disease (IBD) and alimentary lymphoma (ALA) are common gastrointestinal diseases in cats. The very similar clinical signs and histopathologic features of these diseases make the distinction between them diagnostically challenging. ...

A machine learning-based framework to identify type 2 diabetes through electronic health records.

International journal of medical informatics
OBJECTIVE: To discover diverse genotype-phenotype associations affiliated with Type 2 Diabetes Mellitus (T2DM) via genome-wide association study (GWAS) and phenome-wide association study (PheWAS), more cases (T2DM subjects) and controls (subjects wit...

Use of a Machine-learning Method for Predicting Highly Cited Articles Within General Radiology Journals.

Academic radiology
RATIONALE AND OBJECTIVES: This study aimed to assess the performance of a text classification machine-learning model in predicting highly cited articles within the recent radiological literature and to identify the model's most influential article fe...

Classifying smoking urges via machine learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this pape...

Machine learning approaches to personalize early prediction of asthma exacerbations.

Annals of the New York Academy of Sciences
Patient telemonitoring results in an aggregation of significant amounts of information about patient disease trajectory. However, the potential use of this information for early prediction of exacerbations in adult asthma patients has not been system...

Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study.

Annals of the New York Academy of Sciences
Over half of cancer patients receive radiotherapy (RT) as partial or full cancer treatment. Daily quality assurance (QA) of RT in cancer treatment closely monitors the performance of the medical linear accelerator (Linac) and is critical for continuo...

Estimating complicated baselines in analytical signals using the iterative training of Bayesian regularized artificial neural networks.

Analytica chimica acta
The present work deals with the development of a new baseline correction method based on the comparative learning capabilities of artificial neural networks. The developed method uses the Bayes probability theorem for prevention of the occurrence of ...

Network or regression-based methods for disease discrimination: a comparison study.

BMC medical research methodology
BACKGROUND: In stark contrast to network-centric view for complex disease, regression-based methods are preferred in disease prediction, especially for epidemiologists and clinical professionals. It remains a controversy whether the network-based met...

Mycofier: a new machine learning-based classifier for fungal ITS sequences.

BMC research notes
BACKGROUND: The taxonomic and phylogenetic classification based on sequence analysis of the ITS1 genomic region has become a crucial component of fungal ecology and diversity studies. Nowadays, there is no accurate alignment-free classification tool ...