Latest AI and machine learning research in health policy for healthcare professionals.
BACKGROUND: Evidence-based decision-making in healthcare relies heavily on routine health information. However, in many low-income and middle-income countries (LMICs), concerns persist regarding the use of quality routine health data for health service delivery. Moreover, no systematic synthesis currently exists on how the use of quality data influences health service delivery in these settings. T...
OBJECTIVES: To develop and evaluate a machine learning (ML) model that predicts Crohn's disease (CD) patients responsible for the top quartile of healthcare spending. METHODS: De-identified commercial claims (2016-2018) from ~267 000 continuously enrolled members in a Midwestern state were analysed, including 994 CD cases. Monthly data for each patient was aggregated into data points that included...
Maternal undernutrition and micronutrient deficiencies remain pervasive, contributing to adverse pregnancy outcomes and long-term health risks for mot...
Estimating the level of earthquake-induced liquefaction settlements in shallow foundations is essential for assessing seismic risk and designing effec...
Presence of intravenous contrast on computed tomography (CT) scans is often unreliably documented, especially in large research datasets. FALCON is an...
Recent scientific and technological advances have dramatically expanded the possibilities in cardiovascular medicine. Landmark clinical trials have in...
AIM: To compare healthcare utilization and spending among women enrolled in an employer-sponsored, artificial intelligence (AI) structured pelvic care...
BACKGROUND AND AIM: Over recent decades, formal requirements for medical records have been strengthened, for example through patients' rights of acces...
Despite notable progress in understanding the pediatric dental caries area, its multifactorial etiology-including biofilm dynamics, dietary habits, ho...
Timely access to reliable public health data is a critical determinant of effective response to health emergencies, including disease outbreaks, clima...
OBJECTIVES: Evaluate the technical integration and usability of an intraoperative predictive machine learning model for colorectal anastomotic leakage...
OBJECTIVES: Lung cancer is the leading cause of cancer-related mortality worldwide, with poor prognosis largely due to late-stage diagnosis. Current s...
Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, with approximately 25-30 % of cases exhibiting a familial compo...
BACKGROUND: T2-weighted imaging (T2WI) of the liver suffers from prolonged scan times and respiratory motion artifacts. Deep learning (DL)-based recon...
Artificial intelligence (AI) is rapidly reshaping the landscape of health care, from clinical diagnostics and disease surveillance to the prediction o...
The aging population presents a pressing challenge for health care systems, necessitating effective strategies to address the complex needs of older a...
PURPOSE: To accelerate MRI acquisition by incorporating the previous scans of a subject during reconstruction. Although longitudinal imaging constitut...
In every breathless conversation about artificial intelligence (AI), this technology has been heralded as a revolutionary force that will save healthc...
OBJECTIVES: Artificial intelligence (AI) applications in radiology may improve clinical outcomes, but adoption is hindered by limited health economic ...
PURPOSE: Ensemble machine learning (ML) demonstrated potential for improving predictions based on big health care data. We developed and validated int...