Latest AI and machine learning research in public health & policy for healthcare professionals.
Healthcare decision-making requires not only accurate predictions but also insights into how factors influence patient outcomes. While traditional machine learning (ML) models excel at predicting outcomes, such as identifying high-risk patients, they are limited in addressing "what if" questions about interventions. This study introduces the Probabilistic Causal Fusion (PCF) framework, which integ...
INTRODUCTION: Advances in natural language processing (NLP) technologies have gained prominence for extracting relevant clinical information. Savana is a platform capable of analyzing free-text data and interpreting the content of electronic health records (EHRs). OBJECTIVE: To validate the results obtained through NLP by Savana from data of patients with prostate cancer (PC) included in active su...
INTRODUCTION: Focal therapy (FT) has emerged as an intermediate therapeutic strategy between active surveillance (AS) and radical treatments for the m...
BACKGROUND: Delirium remains one of the most consequential complications among critically ill patients in ICUs, exerting profound effects on morbidity...
Inaccurate information regarding cardiovascular disease (CVD) prevention is prevalent on the internet and may influence medical decisions. Artificial ...
Operative management of spinal metastatic disease is largely for symptom palliation rather than curative and revolves around the expectation that post...
The global burden of hepatocellular carcinoma (HCC) has shifted from viral to nonviral etiologies. However, successful antiviral therapy does not full...
Asthma is a heterogeneous condition impacting an estimated 300 million individuals globally. Although inhaled corticosteroids are effective in allevia...
BACKGROUND: There is a wide gap in epilepsy diagnosis, particularly in low- and middle-income countries. We used machine learning models to identify s...
PURPOSE: To develop and validate a neural network-based Kid's Listening Performance Checklist (KLiP) for early identification of listening difficultie...
Cardiovascular disease is the leading cause of global morbidity and mortality, with coronary artery disease representing the primary driver of prematu...
OBJECTIVES: Chemotherapy-induced cardiotoxicity is still a major clinical problem, usually appearing subclinically before structural or symptomatic ca...
Care transitions remain high-risk periods, with up to 28% of patients experiencing adverse events (AEs) or readmissions within 30 days of discharge. T...
Cardiovascular and chronic disease prevention remains limited by episodic, clinic-based assessments that fail to capture physiological changes arising...
OBJECTIVES: This study aimed to assess the current utilization of artificial intelligence (AI) tools among emergency physicians, their attitudes towar...
INTRODUCTION: Since the post-antibiotic era, there has been significant difficulty in treating infectious diseases due to the increase in antimicrobia...
The Korean Longitudinal Study on Digitally Optimized Mental Healthcare is an innovative multicenter trial-ready cohort study. It aims to develop a dig...
Early warning systems (EWSs) for detecting disease outbreaks can help make informed public health decisions and organize necessary responses. During t...
This systematic review examines the applications of artificial intelligence (AI) in preventing obesity, addressing a critical public health issue that...
As predictive analytics become more widely integrated into local public health responses to the United States overdose epidemic, community-based subst...