Public Health & Policy

Artificial intelligence in hospital infection prevention: an integrative review.

BACKGROUND: Hospital-acquired infections (HAIs) represent a persistent challenge in healthcare, contributing to substantial morbidity, mortality, and economic burden. Artificial intelligence (AI) offers promising potential for improving HAIs prevention through advanced predictive capabilities.

Multimodal learning-based speech enhancement and separation, recent innovations, new horizons, challenges and real-world applications.

With the increasing global prevalence of disabling hearing loss, speech enhancement technologies have become crucial for overcoming communication barriers and improving the quality of life for those affected. Multimodal learning has emerged as a powerful approach for speech enhancement and separation, integrating information from various sensory modalities such as audio signals, visual cues, and t...

Identifying chemotherapy beneficiaries in nasal and paranasal sinus cancers: epidemiological trends and machine learning insights.

BACKGROUND: Studies on the epidemiological characteristics, treatment strategies and prognosis of nasal and paranasal sinus cancer are still relativel...

Opportunities and Challenges for Large Language Models in Primary Health Care.

Primary Health Care (PHC) is the cornerstone of the global health care system and the primary objective for achieving universal health coverage. China...

Cholera resurges in Zambia: Challenges and future directions.

Cholera remains a major global health challenge, disproportionately affecting low-income countries with inadequate water, sanitation, and hygiene (WAS...

Fall recognition using a three stream spatio temporal GCN model with adaptive feature aggregation.

The prevention of falls is paramount in modern healthcare, particularly for the elderly, as falls can lead to severe injuries or even fatalities. Addi...

Development and Validation of Models for Preoperative Prediction of Risk and Postoperative Detection of Noninfectious Complications Using Interpretable Machine Learning and Electronic Health Record Data.

OBJECTIVE: To apply interpretable machine learning methodology to electronic health record data to develop models for preoperative risk estimation and...

Explainable AI for Symptom-Based Detection of Monkeypox: a machine learning approach.

BACKGROUND: Monkeypox, a viral zoonotic disease, is an emerging global health concern, with rising incidence and outbreaks extending beyond its endemi...

[Artificial intelligence in Public Health: opportunities, ethical challenges and future perspectives].

Artificial Intelligence (AI) is transforming Public Health by providing innovative tools to address complex global challenges. Its ability to analyze ...

Leveraging artificial intelligence to assess the impact of COVID-19 on the teacher-student relationship in higher education.

The teacher-student relationship has far-reaching implications for educational outcomes at the tertiary level. Teachers contribute to students' succes...

Real-Time Monitoring of Personal Protective Equipment Adherence Using On-Device Artificial Intelligence Models.

Personal protective equipment (PPE) is crucial for infection prevention and is effective only when worn correctly and consistently. Health organizatio...

Machine learning analysis of magnetic covalent organic framework based heterostructures extracted intracellular metabolic fingerprint for direct hypervirulent Klebsiella pneumoniae prediction.

Hypervirulent Klebsiella pneumoniae (hvKP), known for its high virulence and epidemic potential, has emerged as a significant global public health thr...

Exploring the role of breastfeeding, antibiotics, and indoor environments in preschool children atopic dermatitis through machine learning and hygiene hypothesis.

The increasing global incidence of atopic dermatitis (AD) in children, especially in Western industrialized nations, has attracted considerable attent...

Developing cholera outbreak forecasting through qualitative dynamics: Insights into Malawi case study.

Cholera, an acute diarrheal disease, is a serious concern in developing and underdeveloped areas. A qualitative understanding of cholera epidemics aim...

Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data.

Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress...

Deep learning-based normative database of anterior chamber dimensions for angle closure assessment: the Singapore Chinese Eye Study.

BACKGROUND/ AIMS: The lack of context for anterior segment optical coherence tomography (ASOCT) measurements impedes its clinical utility. We establis...

Spatio-temporal epidemic forecasting using mobility data with LSTM networks and attention mechanism.

The outbreak of infectious diseases can have profound impacts on socio-economic balances globally. Accurate short-term forecasting of infectious disea...

Identifying Data-Driven Clinical Subgroups for Cervical Cancer Prevention With Machine Learning: Population-Based, External, and Diagnostic Validation Study.

BACKGROUND: Cervical cancer remains a major global health issue. Personalized, data-driven cervical cancer prevention (CCP) strategies tailored to phe...

Protocol for socioecological study of autism, suicide risk, and mental health care: Integrating machine learning and community consultation for suicide prevention.

INTRODUCTION: Autistic people experience higher risk of suicidal ideation (SI) and suicide attempts (SA) compared to non-autistic people, yet there is...

Formative Research for the Development and Implementation of a Smartphone Application to Report Breaches to the International Code of Marketing of Breast-Milk Substitutes in Mexico.

Almost 40 years after the adoption of the International Code of Marketing of Breast-Milk Substitutes ('the Code') in Mexico, noncompliance persists. I...