Public Health & Policy

Artificial intelligence in neurology: opportunities, challenges, and policy implications.

Neurological conditions are the leading cause of disability and mortality combined, demanding innovative, scalable, and sustainable solutions. Brain health has become a global priority with adoption of the World Health Organization's Intersectoral Global Action Plan in 2022. Simultaneously, rapid advancements in artificial intelligence (AI) are revolutionizing neurological research and practice. T...

Machine learning algorithms to uncover risk factors of breast cancer: insights from a large case-control study.

INTRODUCTION: This large case-control study explored the application of machine learning models to identify risk factors for primary invasive incident breast cancer (BC) in the Iranian population. This study serves as a bridge toward improved BC prevention, early detection, and management through the identification of modifiable and unmodifiable risk factors.

Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data.

The type 1 diabetes community is coalescing around the benefits and advantages of early screening for disease risk. To be accepted by healthcare provi...

Personalized Drug Therapy: Innovative Concept Guided With Proteoformics.

Personalized medicine can reduce adverse effects, enhance drug efficacy, and optimize treatment outcomes, which represents the essence of personalized...

Inadequate Anti-Factor Xa Levels With Daily 40-mg Enoxaparin After Cardiac Surgery.

BACKGROUND: Cardiac surgery patients are at increased risk for venous thromboembolism (VTE). Prevention is the most critical strategy to reduce VTE-as...

Image annotation and curation in radiology: an overview for machine learning practitioners.

"Garbage in, garbage out" summarises well the importance of high-quality data in machine learning and artificial intelligence. All data used to train ...

How Socio-economic Inequalities Cluster People with Diabetes in Malaysia: Geographic Evaluation of Area Disparities Using a Non-parameterized Unsupervised Learning Method.

Accurate assessments of epidemiological associations between health outcomes and routinely observed proximal and distal determinants of health are fun...

Large Language Models and Healthcare Alliance: Potential and Challenges of Two Representative Use Cases.

Large language models (LLMS) emerge as the most promising Natural Language Processing approach for clinical practice acceleration (i.e., diagnosis, pr...

Survival prediction of glioblastoma patients using modern deep learning and machine learning techniques.

In this study, we utilized data from the Surveillance, Epidemiology, and End Results (SEER) database to predict the glioblastoma patients' survival ou...

Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service.

The healthcare sector, characterized by vast datasets and many diseases, is pivotal in shaping community health and overall quality of life. Tradition...

Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge.

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of...

Comparison between a deep-learning and a pixel-based approach for the automated quantification of HIV target cells in foreskin tissue.

The availability of target cells expressing the HIV receptors CD4 and CCR5 in genital tissue is a critical determinant of HIV susceptibility during se...

Building Prediction Models for 30-Day Readmissions Among ICU Patients Using Both Structured and Unstructured Data in Electronic Health Records.

ICU readmissions are associated with poor outcomes for patients and poor performance of hospitals. Patients who are readmitted have an increased risk ...

Incidence and risk factors of inguinal hernia after robot-assisted radical prostatectomy: a retrospective multicenter cohort study in Japan (the MSUG94 group).

To investigate the incidence and risk factors of inguinal hernia (IH) after robot-assisted radical prostatectomy (RARP) using a multicentric database....

Exploring the Potential of Artificial Intelligence in Adolescent Suicide Prevention: Current Applications, Challenges, and Future Directions.

ObjectiveThe global surge in adolescent suicide necessitates the development of innovative and efficacious preventive measures. Traditionally, various...

characterization and molecular epidemiology of spp. isolates from non-HIV patients in Guangdong, China.

BACKGROUND: The burden of cryptococcosis in mainland China is enormous. However, the characterization and molecular epidemiology in Guangdong, a key ...

Improved accuracy and efficiency of primary care fall risk screening of older adults using a machine learning approach.

BACKGROUND: While many falls are preventable, they remain a leading cause of injury and death in older adults. Primary care clinics largely rely on sc...

Use of Health Belief Model-based Deep Learning to Understand Public Health Beliefs in Breast Cancer Screening from Social Media before and during the COVID-19 Pandemic.

Breast cancer is the second leading cause of cancer death for women in the United States. While breast cancer screening participation is the most effe...

Combining Digital and Molecular Approaches Using Health and Alternate Data Sources in a Next-Generation Surveillance System for Anticipating Outbreaks of Pandemic Potential.

Globally, millions of lives are impacted every year by infectious diseases outbreaks. Comprehensive and innovative surveillance strategies aiming at e...

Explainable Deep Learning-Assisted Self-Calibrating Colorimetric Patches for In Situ Sweat Analysis.

Sweat has emerged as a compelling analyte for noninvasive biosensing technology because it contains a wealth of important biomarkers in hormones, orga...