Public Health & Policy

Development and validation of a personalised antipsychotic selection tool for first-line treatment in severe mental illness

Guidance is lacking on choice of first-line antipsychotic for individuals with incident severe mental illness (SMI). Patients may try several before an effective, well-tolerated drug is identified, delaying symptom improvement. We aimed to develop a personalised selection tool to identify the optimum first-line antipsychotic, based on individual sociodemographic and clinical characteristics. Risk ...

Detection of patient metadata in published articles for genomic epidemiology using machine learning and large language models

Patient metadata exist in published articles, but are often dis-connected from genome sequences in databases, limiting their utility for genomic epidemiology. The objective of this study was to develop and evaluate natural language processing methods to facilitate the large-scale detection of patient metadata associated with reports of genome sequencing in published articles, drawing on the case o...

Validation of Natural Language Processing for Surgical Complication Surveillance: Detecting Eleven Postoperative Complications from Electronic Health Records

Postoperative complications (PCs) rates are crucial quality metrics in surgery, as they reflect both patient outcomes, perioperative care effectivenes...

Automated Extraction of Mortality Information from Publicly Available Sources Using Language Models

Mortality is a critical variable in healthcare research, especially for evaluating medical product safety and effectiveness. However, inconsistencies ...

A simple feed forward neural network to predict the 2025 outbreak of measles in the USA

Measles is a highly contagious viral disease associated with a variety of severe complications. Since 1963, widespread usage of a highly effective vac...

Extracting TNFi Switching Reasons and Trajectories From Real-World Data Using Large Language Models

Tumor necrosis factor inhibitors (TNFi) are widely used for auto-immune conditions. Despite their efficacy, many patients switch TNFis due to lack of ...

Causal Forests versus Inverse Probability of Treatment Weighting to adjust for Cluster-Level Confounding: A Parametric and Plasmode Simulation Study based on US Hosptial Electronic Health Record Data

Rapid innovation and new regulations increase the need for post-marketing surveillance of implantable devices. However, complex multi-level confoundin...

Variation and Standardization in Prior Authorization Requirements

Prior authorization (PA) rules are neither regulated nor standardized. To quantify the variation in PA rules of four US health insurers and examine th...

Fine-tuned large language models enhance influenza forecasting

Influenza-like illness (ILI) continues to present significant challenges to global health, highlighting the need for accurate forecasting to guide tim...

Clinicodemographic Prediction of Overall Survival in Patients with Head and Neck Merkel Cell Carcinoma: A Machine Learning Approach

Merkel cell carcinoma (MCC) is a rare cutaneous neuroendocrine malignancy with a higher case-fatality rate than melanoma. The prognosis of MCC is comp...

ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL METHODS FOR MODELLING AND FORECASTING INFLUENZA AND INFLUENZA-LIKE ILLNESS: A SCOPING REVIEW

The persistnt resurgence of influence and influenza-like illness despite concerted vaccination interventions is a global health burden, thus necessita...

Estimating the worst-case scenario for malaria parasite rate in sub-Saharan Africa

Malaria remains a leading cause of morbidity and mortality worldwide, with sub-Saharan Africa bearing the highest burden. Stalled progress under an in...

Fast and Trustworthy Nowcasting of Dengue Fever: A Case Study Using Attention-Based Probabilistic Neural Networks in São Paulo, Brazil

Nowcasting methods are crucial in infectious disease surveillance, as reporting delays often lead to underestimation of recent incidence and can impai...

Machine learning prediction for early-stage melanoma outcomes: recurrence-free survival, disease-specific survival, and overall survival

This study compared machine-learning models for predicting recurrence-free survival (RFS), disease-specific survival (DSS), and overall survival (OS) ...

AI-Driven Pharmacovigilance and Molecular Profiling of Fluoroquinolone-Associated Cardiotoxicity in the UAE: A Geospatial and Machine Learning Analysis with Structural Modification Strategies (2018-2023)

Fluoroquinolones, while clinically indispensable, carry underappreciated cardiovascular risks, particularly QT prolongation and life-threatening arrhy...

Development and validation of diagnostic and prognostic prediction tools for dental caries in young children: A protocol

Dental caries is the most common oral disease worldwide, affecting up to 90% of children globally. It can lead to pain, infection, and impaired qualit...

The allostatic overload in pregnancy during the COVID-19 pandemic and potential effects on the health of the mother-child dyad: Study Protocol

Allostatic load refers to the cumulative burden of stress and life events that involve the interaction of various physiological systems at differing l...

AI-Enabled Diagnostic Prediction within Electronic Health Records to Enhance Biosurveillance and Early Outbreak Detection

Detecting infectious disease outbreaks promptly is crucial for effective public health responses, minimizing transmission, and enabling critical inter...

Identifying profiles, trajectories, burden, social and biological factors in 3.3 million individuals with multimorbidity in England

Multimorbidity, the co-occurrence of multiple chronic conditions in an individual, has become a global health challenge affecting populations in high-...

Kolmogorov-Arnold Network for Atherosclerotic Cardiovascular Disease Risk Prediction

Assessing the risk of future atherosclerotic cardiovascular disease (ASCVD) is crucial in clinical practice, yet it continues to pose significant chal...