Quality is becoming a critical issue for radiology. Measuring and improving quality is
essential not only to ensure optimum effectiveness of care and comply with increasing …

The huge amount of information that needs to be assimilated in order to keep pace with the
continued advances in modern medical practice can form an insurmountable obstacle to the …

Purpose: To retrospectively determine whether a Bayesian network (BN) computer model
can accurately predict the probability of breast cancer on the basis of risk factors and …

Background Many primary malignancies spread via lymphatic dissemination, and accurate
staging therefore still relies on surgical exploration. The purpose of this study was to explore …

Purpose: To determine whether a Bayesian network trained on a large database of patient
demographic risk factors and radiologist-observed findings from consecutive clinical …

Images are pervasive in biomedicine, providing key information used for understanding the
phenotype of disease. Biomedical imaging informatics is a field that involves computational …

Medical knowledge is growing at an explosive rate. While the availability of pertinent data
has the potential to make the task of diagnosis more accurate, it is also increasingly …

While the use of machine learning methods in clinical decision support has great potential
for improving patient care, acquiring standardized, complete, and sufficient training data …

We investigated Bayesian network structure learning and probability estimation from
mammographic feature data in order to classify breast lesions into different pathological …

In this paper, we present the validation and verification of a machine-learning based
Bayesian network of breast pathology co-occurrence. The present/not present occurrences …