Public Health & Policy

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 validation of a machine learning model for predicting the risk of current depression in physically inactive adults in the United States.

Depression is a major global public health concern, with physical inactivity recognized as a key modifiable risk factor. However, tools for predicting...

Uncovering predictors of bipolar II conversion to bipolar I: A machine learning analysis of national health records in Taiwan.

BACKGROUND: Bipolar II disorder (BD-II) can progress to bipolar I disorder (BD-I), carrying profound psychosocial consequences. However, limited resea...

Distributed Precision Stroke Care: Artificial Intelligence-Driven Stroke Management Using Multimodal Sensor Data.

Delays in stroke diagnosis contribute to long-term disability. Many patients still face barriers to effective risk factor management, timely detection...

Tridimensional analysis of the normal upper airways in non-dentofacial deformity patients.

OBJECTIVES: To establish normative three-dimensional airway measurements in patients without dentofacial deformities (DDFs) or obstructive sleep apnea...

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...

Cardiovascular disease prevention in China: challenges and opportunities in the artificial intelligence-enabled digital health era.

Over the past decade, substantial progress has been made in China in improving health-care infrastructure and enacting national policies focused on th...

Accelerometer-measured intensity-specific physical activity, genetic susceptibility, and back pain risk: a UK Biobank cohort study.

BACKGROUND CONTEXT: Current clinical guidelines lack clear, quantitative recommendations on intensity-specific physical activity (PA) levels for preve...

Sleep disturbance recorded via wearable sensors predicts depression severity 9 years later.

BACKGROUND: Major depressive disorder (MDD) is prevalent and poses major public health implications. Autonomic nervous system (ANS) dysregulation and ...

Exploring biosensors: Distinctive features and emerging applications.

Biosensors have become essential analytical tools that integrate biological recognition elements with physical transducers to detect specific analytes...

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 ...

Age-specific modifiable determinants and nonlinear risk patterns of habitual low back pain in a large medical check-up cohort: a cross-sectional and longitudinal analysis.

BACKGROUND: Age-related mechanisms underlying habitual low-back pain (LBP) remain poorly defined, with most studies pooling heterogeneous populations ...

Precision Nutritional Genomics, Gut Microbiota and Artificial Intelligence in Chronic Kidney Disease.

Chronic kidney disease (CKD) is a prevalent global health issue, and nutritional management of CKD is an integral component through all stages of the ...

Innovative Approaches to Head and Neck Cancer Diagnosis.

Diagnosis, positioned between disease prevention and treatment, is essential for head and neck cancer management. Delays in diagnosis contribute to di...

Multidrug-resistant pathogens in UTIs: A concise review of epidemiology, diagnostics, and patent landscaping for precision healthcare.

UTIs are regarded as the second most prevalent global problem, with 150 to 250 million cases reported annually. Poor hygiene, anatomical abnormalities...

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...