Public Health

Letter to the editor: "How do experts classify sepsis cases for sepsis surveillance? Lessons learned from a behavioural artificial intelligence technology (BAIT) approach".

Behavioural Artificial Intelligence Technology (BAIT) has recently been proposed to codify expert reasoning for sepsis surveillance. We provide preliminary cross-institutional data from two Southeast Asian hospitals (N = 1042 suspected sepsis episodes), demonstrating challenges in multi-national generalizability, misclassification of non-infectious inflammatory states, and underutilization of proc...

Large language models accurately extract aortic information from abdominal imaging reports in a large, real-world database.

OBJECTIVE: Maintaining robust surveillance programs for abdominal aortic aneurysms (AAAs) is important, but these programs are expensive and labor-intensive, typically requiring manual data review by trained health care professionals. Studies have shown that natural language processing software can assist in these functions, but each task-specific algorithm requires human-directed training before ...

Dengue forecasting and outbreak detection in Brazil using LSTM: integrating human mobility and climate factors.

BACKGROUND: Dengue fever is a major global health concern, with Brazil experiencing recurrent and severe outbreaks due to its favorable climate factor...

Modeling and forecasting vibrio vulnificus concentration of long-range dependence on marine environmental conditions.

Vibrio vulnificus (vvh) is an epidemiologically significant bacterium that naturally occurs in coastal waters under favorable environmental conditions...

Strengthening Signal Detection in Pharmacovigilance by Using International Nonproprietary Name (INN) Stems.

'Stems', which mark pharmacological relationships between substances, form the backbone of the International Nonproprietary Name (INN) system, develop...

Development and external validation of interpretable machine learning models for personalized multiple treatment recommendations in non-small cell lung cancer.

OBJECTIVE: Treatment decision-making for non-small cell lung cancer (NSCLC) is complex, necessitating individualized decision-support tools to improve...

Prognostic prediction of hepatoblastoma in children: development and validation of machine learning models-an SEER-based study.

Hepatoblastoma (HB) is the most common primary malignant liver tumor in children. Although the incidence is low, it is a serious threat to children's ...

Cervical cancer diagnostics: non-coding RNAs and biosensors to AI-derived methods.

Cervical cancer ranks fourth in terms of cancer mortality among women. The most important risk factor for cervical cancer is infection with HPV 16 and...

When AI Joins the Social Media Conversation: Exploring the Impact of Simulated AI-Assisted Comments on Health Risk Perceptions and Behaviors.

Misinformation on social media has long been a pressing concern, and its spread is often exacerbated by social endorsement metrics such as likes and s...

Bias or Best Fit? A Comparative Analysis of the SEER and NCDB Data Sets in Single-model Machine Learning for Predicting Osteosarcoma Survival Outcomes.

BACKGROUND: Machine-learning models are increasingly used in orthopaedic oncology to predict survival outcomes for patients with osteosarcoma. Typical...

Long-Term Outcomes and Cost-Effectiveness of Artificial Intelligence for Breast Cancer Screening: A Modeling Study.

OBJECTIVES: Artificial intelligence (AI)-assisted breast cancer screening may improve diagnostic accuracy; however, the long-term health outcomes and ...

Artificial intelligence and infectious disease diagnostics: state of the art and future perspectives.

Artificial intelligence (AI) is reshaping infectious disease diagnostics by supporting clinical decision making, optimising laboratory and clinical wo...

Artificial intelligence and infectious diseases: an evidence-driven conceptual framework for research, public health, and clinical practice.

As artificial intelligence (AI) is projected to radically shape health care, its role in infectious disease prevention and management is drawing atten...

Artificial intelligence and infectious diseases: tackling antimicrobial resistance, from personalised care to antibiotic discovery.

Antimicrobial resistance (AMR) is an intractable problem that has the potential to significantly limit advances in human health. Recently, the UN Gene...

A framework using large time series model for early warning of infectious diseases.

OBJECTIVE: Infectious diseases controlling system is indispensable for weaken the damage to the people's life and property security caused by infectio...

Clinical implications of computer-aided real-time size estimation of colorectal polyps during colonoscopy: a prospective study.

Accurate polyp size estimation during colonoscopy is crucial for clinical decision making, follow-up, and implementation of cost-saving strategies. Ob...

Health risk of PM2.5-bound heavy metals in a megacity in South China: Comparison between before and after the outbreak of COVID-19.

The COVID-19 pandemic led to three years (2020-2022) of human activity restrictions in China, significantly impacting urban air quality, yet long-term...

Navigating Medical Device Safety: Current Status, Challenges, and Future Regulatory Directions.

Medical devices are indispensable in modern healthcare. They enable the prevention, diagnosis, and treatment of diseases while enhancing patient outco...

Using artificial intelligence as a technological tool in gynecologic and obstetric health: A narrative literature review.

Maternal mortality remains a critical global public health issue, particularly in low- and middle-income settings where failures in surveillance, earl...

How do experts classify sepsis cases for sepsis surveillance? Lessons learned from a Behavioural Artificial Intelligence Technology (BAIT) approach.

OBJECTIVES: To identify relevant objective variables for retrospective identification of 'suspected infection' and sepsis, using behavioural artificia...