We investigate the viability of statistical relational machine learning algorithms for the task of identifying malignancy of renal masses using radiomics-based imaging features. Features characterizing the texture, signal intensity, and other relevan...
Journal of the American Medical Informatics Association : JAMIA
Nov 1, 2018
OBJECTIVE: Develop an approach, One-class-at-a-time, for triaging psychiatric patients using machine learning on textual patient records. Our approach aims to automate the triaging process and reduce expert effort while providing high classification ...
Clinical orthopaedics and related research
Oct 1, 2018
BACKGROUND: Several studies have identified prognostic factors for patients with chondrosarcoma, but there are few studies investigating the accuracy of computationally intensive methods such as machine learning. Machine learning is a type of artific...
INTRODUCTION: Most risk assessment tools assume that the impact of risk factors is linear and cumulative. Using novel machine-learning techniques, we sought to design an interactive, nonlinear risk calculator for Emergency Surgery (ES).
Artificial intelligence and machine learning have the potential to revolutionize the delivery of health care. But designing machine learning-based decision support systems is not a merely technical challenge. It also requires attention to bioethical ...
BACKGROUND: It is unclear whether radiomic phenotypes of brain metastases (BM) are related to radiation therapy prognosis. This study assessed whether a convolutional neural network (CNN)-based radiomics model which learned computer tomography (CT) i...
OBJECTIVES: Early prediction of undesired outcomes among newly hospitalized patients could improve patient triage and prompt conversations about patients' goals of care. We evaluated the performance of logistic regression, gradient boosting machine, ...
International journal of health care quality assurance
Jun 11, 2018
Purpose Resilience engineering, job satisfaction and patient satisfaction were evaluated and analyzed in one Tehran emergency department (ED) to determine ED strengths, weaknesses and opportunities to improve safety, performance, staff and patient sa...
Journal of the American College of Radiology : JACR
Mar 1, 2018
The field of diagnostic decision support in radiology is undergoing rapid transformation with the availability of large amounts of patient data and the development of new artificial intelligence methods of machine learning such as deep learning. They...