Public Health

KLiP: A neural network-based surveillance tool for early identification of listening difficulties in children aged 3-6 years.

PURPOSE: To develop and validate a neural network-based Kid's Listening Performance Checklist (KLiP) for early identification of listening difficulties in children aged 3-6 years, and to evaluate its effectiveness as a surveillance tool in real-world settings. METHODS: Development followed a multi-phase process: (1) systematic resource review to create preliminary items, (2) expert evaluation for ...

From Nutritional Epidemiology to Clinical Translation: Machine Learning Uncovers Antioxidant-Driven Prevention Strategies for Mitigating Cardiovascular-Arthritis Multimorbidity.

BACKGROUND: The aim of this study was to construct a machine learning (ML) model to predict the effect of dietary antioxidants on cardiovascular-arthritis comorbidity. METHODS: In this study, 44 dietary antioxidants were selected based on the National Health and Nutrition Examination Survey data from 2007 to 2010 and from 2017 to 2018, and demographic covariates such as gender and age were include...

The global epidemiology of botulism outbreaks from open-source intelligence, 2017-2024.

BACKGROUND: Botulism is a rare but potentially fatal illness caused by botulinum neurotoxin, with outbreaks reported globally in humans, animals, and ...

Novel Psychoactive Substances: Slaying the Dragon With Artificial Intelligence.

BACKGROUND: The emergence of novel psychoactive substances (NPSs) has overwhelmed forensic, health care, and regulatory systems. Conventional analytic...

Advances in occult cancer screening for patients with unprovoked venous thromboembolism: A narrative review of epidemiology, risk, and clinical strategies.

BACKGROUND: Venous thromboembolism (VTE) and cancer exhibit a bidirectional correlation. The probability of detecting occult cancer in unprovoked VTE ...

Machine learning to improve analysis of disability in electronic health records: an untapped opportunity for health inequities research.

Electronic Health Records (EHRs) are a leading source of epidemiological data, but often lack information on a patient's disability status. This gap h...

Development of an Infectious Disease Vulnerability Index (IDVI) for dairy farms: A data-driven approach to assessing risk and informing biosecurity practices.

Dairy farms are vulnerable to a wide range of infectious diseases, which can have significant economic and public health consequences. However, there ...

Circadian rhythm: An emerging force for colorectal cancer treatment.

The relationship between circadian rhythm disorder and the occurrence and progression of colorectal cancer (CRC) has received attention. The circadian...

Integrating Kolmogorov-Arnold networks with ordinary differential equations for efficient, interpretable, and robust deep learning: Epidemiology of infectious diseases as a case study.

This study extends universal differential equation (UDE) frameworks by integrating the Kolmogorov-Arnold Network (KAN) with ordinary differential equa...

Leveraging artificial intelligence for surgical site infection surveillance: A comparison of 5 large language models.

We conducted a retrospective study to evaluate the performance of 5 large language models in detecting surgical site infections (SSIs), compared with ...

Molecular diagnostics in clinical microbiology: Advances, applications, and future directions.

Molecular diagnostics have transformed clinical microbiology by enabling rapid, accurate, and highly sensitive detection of infectious agents, signifi...

Use of human data for risk assessment of pesticides: A review including an evaluation of trichlorfon as case study.

Pesticides are suspected to cause health effects in humans, but human-based data on their toxicity are often insufficient to establish associations an...

Self-reported periodontitis: A multiethnic community-based validation study.

OBJECTIVE: Self-reported periodontal measures offer a practical alternative to clinical examinations in large-scale studies, but few validation effort...

Artificial intelligence-enabled electrocardiographic sex discordance and the risk of incident atrial fibrillation: A multinational cohort study.

BACKGROUND: Binary classification of sex fails to capture the sex-related continuum of atrial fibrillation (AF) risk. OBJECTIVE: This study aimed to d...

The global epidemiology of acute myeloid leukaemia.

Acute myeloid leukaemia (AML) is an aggressive haematological malignancy with an incidence that increases with age and varies widely across regions ow...

A machine learning based framework for identifying consumer product injuries from social media data.

BACKGROUND: Safety is a critical aspect of consumer products. However, there are millions of product related injuries reported each year. Traditional ...

Cross-modality fusion of hematology-based digital and molecular biomarkers for intelligent epidemiological screening and surveillance of hepatocellular carcinoma.

Early detection of hepatocellular carcinoma (HCC) remains a critical challenge due to the disease's complexity and the limitations of current screenin...

Building and validating a machine learning model to predict coronary heart disease risk based on non-invasive indicators.

BACKGROUND: Coronary heart disease (CHD) remains a leading global cause of death. Early identification of high-risk individuals and timely interventio...

Geometric deep learning for local growth prediction on abdominal aortic aneurysm surfaces.

Abdominal aortic aneurysms (AAAs) are progressive focal dilatations of the abdominal aorta. AAAs may rupture, with fatal consequences in >80% of cases...

Pressure injury surveillance in the intensive care unit: Development, validation, and clinical application of a natural language processing algorithm.

INTRODUCTION: Pressure injuries (PIs) are one of the most common hospital-acquired complications in patients admitted to an intensive care unit (ICU)....