Public Health

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 prognosis. This study aimed to develop and externally validate interpretable machine learning models to predict multiple treatment recommendations (surgery, radiotherapy and chemotherapy) and assess their prognostic implications. METHODS: We utilize...

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 HPV 18. The prevalence and mortality rates of this cancer are much higher in countries with low and medium development indices than in developed countries. Improving health, access to vaccination, and screening tests are highly helpful in preventing...

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...

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...

AI-driven diagnosis of Lassa fever: Evidence from Nigerian clinical records.

BACKGROUND: Neglected Tropical Diseases (NTDs), particularly Lassa fever, remain a significant public health challenge in Nigeria, often presenting wi...

Cancer vaccines: Discovery, development, and challenges for clinical translation.

Cancer vaccines stimulate antitumor immunity by delivering tumor antigens and, in recent years, have emerged as a promising therapeutic strategy again...

Leveraging machine learning for predicting Opisthorchis viverrini infection in cats: A tool for veterinary epidemiology.

Opisthorchis viverrini (Ov) infection is a major public health concern in the Greater Mekong Subregion, with cats as key reservoir hosts. Although mac...

Intraductal Papillary Mucinous Neoplasm and Pancreatic Cancer: Opportunity Knocks Twice.

Pancreatic cystic lesions are widely recognized as harbingers of pancreatic cancer. Intraductal papillary mucinous neoplasm (IPMN) is the most common ...

Machine Learning Risk Stratification for Older Breast Cancer Survivors: Clinical Care Implications.

OBJECTIVE: To develop and validate a clinical risk prediction algorithm to identify breast cancer survivors at high risk for adverse outcomes. STUDY S...

Crafting an innovative one health-aligned machine learning framework for neglected tropical diseases elimination.

INTRODUCTION: Strategic investment in new interventions is crucial for controlling and eliminating NTDs. However, selecting the optimal intervention c...

Identification of diagnostic biomarkers and dissecting immune microenvironment with crosstalk genes in the POAG and COVID-19 nexus.

An underlying association between primary open-angle glaucoma (POAG) and COVID-19 has been hypothesized, but the causal link and shared mechanisms rem...