Latest AI and machine learning research in surveillance for healthcare professionals.
OBJECTIVE: To apply interpretable machine learning methodology to electronic health record data to develop models for preoperative risk estimation and postoperative detection of noninfectious postoperative complications. SUMMARY BACKGROUND DATA: We previously developed preoperative risk and postoperative detection models for the surveillance of postoperative infections. The purpose of the present ...
Early-life exposure to endocrine-disrupting chemicals (EDCs) may contribute to small vulnerable newborns, including conditions such as being small for gestational age (SGA) and preterm birth (PTB), yet evidence remains limited. This study, which is based on 739 mother-infant pairs in the Chinese Jiashan Birth Cohort (2016-2018), including 39 SGA and 38 PTB cases, employed interpretable machine lea...
BACKGROUND: Monkeypox, a viral zoonotic disease, is an emerging global health concern, with rising incidence and outbreaks extending beyond its endemi...
Artificial Intelligence (AI) is transforming Public Health by providing innovative tools to address complex global challenges. Its ability to analyze ...
A standard for reporting genetic pathology results currently does not exist as a consensus. While effective reports are produced, there is lack of con...
Malignant cerebral edema (MCE) is a severe complication of acute ischemic stroke, with high mortality rates. Early and accurate prediction of MCE is c...
Multimodal artificial intelligence (AI) has the potential to revolutionise healthcare by enabling the simultaneous processing and integration of vario...
FaciaVox is a multimodal biometric dataset that consists of face images and voice recordings under both masked and unmasked conditions. The term ``Fac...
Almost 40 years after the adoption of the International Code of Marketing of Breast-Milk Substitutes ('the Code') in Mexico, noncompliance persists. I...
Despite current surveillance and sanitation strategies, foodborne pathogens continue to threaten the food industry and public health. Whole genome seq...
With the rapid advancement of artificial intelligence in health care, large language models (LLMs) demonstrate increasing potential in medical applic...
OBJECTIVES: Adherence to established reporting guidelines can improve clinical trial reporting standards, but attempts to improve adherence have produ...
This protocol outlines a systematic review and meta-analysis examining the effectiveness of fully automated, AI-driven chatbot interventions in reduci...
BACKGROUND: Hepatocellular carcinoma (HCC) is often diagnosed using gadoxetate disodium-enhanced magnetic resonance imaging (EOB-MRI). Standardized re...
Early detection of malignant thyroid nodules is crucial for effective treatment, but traditional diagnostic methods face challenges such as variabilit...
Clonorchis sinensis (C. sinensis) is mainly prevalent in Northeast and South China, with Guangxi being the most severely affected region. This study a...
BACKGROUND: While the COVID-19 pandemic has induced massive discussion of available medications on social media, traditional studies focused only on l...
OBJECTIVE: African swine fever (ASF) is a lethal and highly contagious transboundary animal disease with the potential for rapid international spread....
Epidemiologists often handle large datasets with numerous variables and are currently seeing a growing wealth of techniques for data analysis, such as...
BACKGROUND: The surge in artificial intelligence (AI) interventions in primary care trials lacks a study on reporting quality.